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Statistics By Jim

Making statistics intuitive

How to do t-Tests in Excel

By Jim Frost 120 Comments

Excel can perform various statistical analyses, including t-tests. It is an excellent option because nearly everyone can access Excel. This post is a great introduction to performing and interpreting t-tests even if Excel isn’t your primary statistical software package.

In this post, I provide step-by-step instructions for using Excel to perform t-tests. Importantly, I also show you how to select the correct form of t-test, choose the right options, and interpret the results. I also include links to additional resources I’ve written, which present clear explanations of relevant t-test concepts that you won’t find in Excel’s documentation. And, I use an example dataset for us to work through and interpret together!

• Two group means are different.
• Paired means are different.
• One mean is different from a target value.

For more information about the types of t-tests you can use, read my T-Test Overview post. Also, learn how it compares to the Z Test .

Install the Data Analysis ToolPak in Excel

The Data Analysis ToolPak must be installed on your copy of Excel to perform t-tests. To determine whether you have this ToolPak installed, click Data in Excel’s menu across the top and look for Data Analysis in the Analyze section. If you don’t see Data Analysis, you need to install it. Don’t worry. It’s free!

Note : Excel cannot perform a one-sample t-test .

Two-Sample t-Tests in Excel

Two-sample t-tests compare the means of precisely two groups—no more and no less! Typically, you perform this test to determine whether two population means are different. For example, do students who learn using Method A have a different mean score than those who learn using Method B? This form of the test uses independent samples. In other words, each group contains a unique set of people or items.

Statisticians consider differences between group means to be an unstandardized effect size because these values indicate the strength of the relationship using values that retain the natural units of the dependent variable. Cohen’s d is the corresponding standardized effect size and it’s appropriate to report in some cases. Effect sizes help you understand how important the findings are in a practical sense. To learn more about unstandardized and standardized effect sizes, read my post about Effect Sizes in Statistics .

The standard form tests the following hypotheses:

• Null : The two population means are equal.
• Alternative : The two population means are not equal.

If the p-value is less than your significance level (e.g., 0.05), you can reject the null hypothesis. The difference between the two means is statistically significant. Your sample provides strong enough evidence to conclude that the two population means are different.

For more information about the null and alternative hypotheses and other hypothesis testing terms, see my Hypothesis Testing Overview .

Also, learn about the difference between descriptive statistics and inferential statistics .

t-Tests for Equal and Unequal Variances

You’ll notice that Excel has two forms of the two-sample t-test. One that assumes equal variances and the other that assumes unequal variances. Variances and the closely related standard deviation are measures of variability . All t-tests assume you obtained data from normally distributed populations. However, the conventional t-test also assumes the standard deviations/variances for both groups are equal. Another form of the test, known as Welch’s t-test, does not assume equal variances.

As an aside, thanks to the central limit theorem , you can safely use t-tests to analyze nonnormal data when have ~20 or more observations per group.

Which One to Use?

Advice for using either the equal or unequal variances form of the 2-sample t-test varies because this issue is more complicated than it first appears. Some analysts advise using an F-test to determine whether the variances are unequal. And, Excel does offer the F-test Two-Sample for Variances. However, using additional tests always increases the probability of both false positives and false negatives (a.k.a, Type I and Type II errors ).

Additionally, if you have a large sample size, the f-test has more statistical power . This condition can cause the test to identify an inconsequential difference as being statistically significant. That’s the difference between practical significance and statistical significance . Conversely, small sample sizes can fail to detect a substantial difference between variances.

When you have an equal, or nearly equal, number of observations in both groups and a moderate sample size, t-tests are robust to differences between variances. If you find one group has twice the variance of another group, it might be time to worry! However, you don’t need to worry about smaller differences.

Other analysts suggest always using the form of the t-test that assumes unequal variances. If you use this approach when the variances are equal, you lose a trivial amount of statistical power, but you’ll be better off when the variances are not equal.

If you have unequal variances and unequal samples sizes, it’s vital to use the unequal variances version of the 2-sample t-test!

Step-by-Step Instructions for Running the Two-Sample t-Test in Excel

Let’s conduct a two-sample t-test! This test is also known as the independent samples t-test . Click the link to learn more about its hypotheses, assumptions, and interpretation.

Our hypothetical scenario is that we are comparing scores from two teaching methods. We drew two random samples of students. One sample comprises students who learned using Method A while the other sample learned using Method B. These samples contain entirely different students. Now, we want to determine whether the two means are different. Download the CSV file that contains all data for both t-test examples in this post: t-TestExamples .

To perform a 2-sample t-test in Excel, arrange your data in two columns, as shown below.

• In Excel, click Data Analysis on the Data tab.
• From the Data Analysis popup, choose t-Test: Two-Sample Assuming Equal Variances .
• Under Input , select the ranges for both Variable 1 and Variable 2 .
• In Hypothesized Mean Difference , you’ll typically enter zero. This value is the null hypothesis value, which represents no effect. In this case, a mean difference of zero represents no difference between the two methods, which is no effect.
• Check the Labels checkbox if you have meaningful variable names in row 1. This option makes the output easier to interpret. Ensure that you include the label row in step #3.
• Excel uses a default Alpha value of 0.05, which is usually a good value. Alpha is the significance level. Change this value only when you have a specific reason for doing so.

For the example data, your popup should look like the image below:

Interpreting the Two-Sample t-Test Results

If the p-value is less than your significance level, the difference between means is statistically significant. Excel provides p-values for both one-tailed and two-tailed t-tests.

One-tailed t-tests can detect differences between means in only one direction. For example, a one-tailed test might determine only whether Method B is greater than Method A. Two-tailed tests can detect differences in either direction—greater than or less than. There are additional drawbacks for using one-tailed tests—so I’ll stick with the standard two-tailed results. To learn more, read my post about one-tailed and two-tailed tests .

For our results, we’ll use P(T<=t) two-tail, which is the p-value for the two-tailed form of the t-test. Because our p-value (0.000336) is less than the standard significance level of 0.05, we can reject the null hypothesis. Our sample data support the hypothesis that the population means are different. Specifically, Method B’s mean is greater than Method A’s mean.

Paired t-Tests in Excel

Paired t-tests assess paired observations, which are often two measurements on the same person or item. Statisticians call these dependent samples. Suppose you gather a random sample of people. You give them all a pretest, administer a treatment, and then perform a posttest. Each subject has a pretest and posttest score. Or, perhaps you have a sample of wood boards, and you paint half of each board with one paint and the other half with different paint. Then, you measure the paint durability for both types of paint on all the boards. Each board has two paint durability scores.

In both cases, you can use a paired t-test to determine whether the difference between the means of the two sets of scores is statistically significant.

Unlike independent t-tests, paired t-tests use the same people or items in both groups. One way to determine whether a paired t-test is appropriate for your data is if each row in the dataset corresponds to one person or item. For our pretest/posttest example, we measure each subject before and after the experiment and placed the measurements for an individual on one row.

Related posts : Independent and Dependent Samples and Paired T Test

Step-by-Step Instructions for Running the Paired t-Test in Excel

For this example, imagine that we have a training program, and we need to determine whether the difference between the mean pretest score and the mean posttest score is significantly different.

To perform a paired t-test in Excel, arrange your data into two columns so that each row represents one person or item, as shown below. Note that the analysis does not use the subject’s ID number.

• From the Data Analysis popup, choose t-Test: Paired Two Sample for Means .
• Check the Labels checkbox if you have meaningful variables labels in row 1. This option helps make the output easier to interpret. Ensure that you include the label row in step #3.

Interpreting Excel’s Paired t-Test Results

If the p-value is less than your significance level, the difference between means is statistically significant. Again, Excel provides p-values for both one-tailed and two-tailed t-tests—and we’ll stick with the two-tailed result. For information about the other statistics, click the links in the 2-sample t-test section.

For our results, we’ll use P(T<=t) two-tail, which is the p-value for the two-tailed form of the t-test. Because our p-value (0.002221) is less than the standard significance level of 0.05, we can reject the null hypothesis. Our sample data support the hypothesis that the population means are different. Specifically, the Posttest mean is greater than the Pretest mean.

What Excel’s t-Tests Do Not Include

As nice as it is to be able to perform t-tests in Excel, it leaves out some essential features. Notably, Excel cannot create confidence intervals. The means in these analyses are the point estimates for the population means. However, thanks to random error, the sample means never precisely equal the population mean. There is a margin of error around the estimates. Confidence intervals use a margin of error to calculate a range of values that is likely to contain the actual population mean for each group. Learn more about confidence intervals .

Excel also doesn’t calculate the estimated difference between the means. The difference between the means is the effect size for the analysis—an important value to know. By using a formula in Excel, you can easily calculate the difference between means. However, it would be nice to have a confidence interval for this difference too. For more information, read my post about using confidence intervals to assess differences between means .

Finally, Excel, strangely, does not provide a one-sample t-test! In some cases, you might have a single sample of data and need to determine whether it is different from a target value. For example, you might measure the strength of a product and use a one-sample t-test to determine whether it is significantly different from an important strength value.

t-Tests can compare up to two groups. If you have three or more groups, you’ll need to use ANOVA. For more information, see my posts about how to do one-way ANOVA in Excel  and how to do two-way ANOVA in Excel ! Learn about the T Distribution .

If you want to learn more about how t-tests work, read my post 1-sample, 2-sample, and Paired t-Tests .

If you’re learning about hypothesis testing and like the approach I use in my blog, check out my Hypothesis Testing book! You can find it at Amazon and other retailers.

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Reader Interactions

February 27, 2024 at 11:03 pm

Hey Jim, Thanks fir this post. Had question regarding paired t-test. Due to an intervention, I am using paired t-test to understand the impact on sales. Lets say we already know for that our business has gone down. So the difference in mean sales would be negative. How to factor in the %age of declining business factor, so that negative difference of mean is not an indicative of intervention. Thank you

February 27, 2024 at 11:46 pm

I think I’m understanding your question, but if I’m not let me know.

You have an intervention but you also know that there’s a negative sales trend but presumably your intervention was to increase sales. So, you want to see if the effect was significantly positive even if sales declined. Basically, sales might have gone down but less than expected due to the overall decline thanks to your intervention?

One approach would be to perform a paired t-test as you mention but change the null hypothesis to account for the downturn. Suppose business declines by 10% and the average pre-test sale is 50. Therefore, you’d expect a drop of -5 from the pre-test to post-test. Usually, a paired t-test’s null hypothesis evaluates whether the mean difference = 0. In your case, you change the null so it tests whether the mean difference = -5. (Of course, use your own value.) So, if the actual mean difference from the data was -1, the paired t-test would see if that is greater than -5. It might be significant even though it is negative. A significant result implies that the post-test values are significantly higher than what would be expected under the null hypothesis of a -5 decline.

The success of this approach heavily depends on how accurately the expected decline (-5 in my example) is estimated. Miscalculation or misestimation could lead to incorrect conclusions. Most statistical software will allow you to change the value in the null hypothesis.

A more sophisticated approach would be to consider using a regression model that includes control variables that can account for other factors influencing sales, like market trends, seasonal effects, or economic conditions. This method allows for the quantification of the specific impact of the intervention while controlling for other factors. It’s a powerful approach for isolating the effect of the intervention from general trends and seasonal effects.

Another possibility is to perform an interrupted time series analysis (ITSA). If you have sales data collected at multiple points in time before and after the intervention, ITSA can be a powerful method. It allows you to model the sales trend over time and assess how the intervention has impacted this trend. This is particularly useful if there’s an ongoing decline, as it helps in understanding whether the intervention slowed down, halted, or reversed the decline.

The latter two approaches are more complex and require more data. It depends if the simple paired t-test with adjusted null hypothesis is good enough for your needs or not and how much time and resources you have for doing a more complex analysis.

February 28, 2024 at 12:08 am

Thanks Jim for detail feedback 🙂 Super insightful

February 28, 2024 at 12:13 am

You’re very welcome!

February 25, 2024 at 9:57 pm

Thanks you so much for this. Very clear. Can you tell me if you have found a way to have a malleable input? I have changing data and would like my dashboard to update without having ro rerun the T tests. Is there a way to do this that you know of?

February 25, 2024 at 10:42 pm

I’m not sure what you mean by “malleable input?”

It sounds like you might want to continually perform t-tests as new data comes in? If so, that’s not recommended because it tends to generate false positives. Suppose you have a case where the null hypothesis is true. Then you continually perform t-tests as data comes in. Most results are negative. But you might get a positive result at some point purely due to chance. You’ll stop testing and state you have significant results even though all you did was test through a bunch of negative results until you got one positive result by chance.

Instead, define how many samples you’ll collect. Collect the data. Then perform a single t-test.

July 3, 2022 at 7:31 pm

Hi Jim, I wasn’t sure how to start a new comment but found I can post a reply. I am doing comparisons of gene expression levels between normal and diseased samples. There are 100 genes to do individual t-tests, sample sizes are 15 normal and 12 diseased. I initially used two sample assuming equal variances after doing random F-tests. However, I realised that there might be a few with unequal variances. Should I do an F-test for each gene? Is it possible to do both equal and unequal variances depending on the f-test result or should I just simply assume unequal variances for all? Also, is the Benjamini-Hochberg method the best correction to use? Thank you in advance.

March 1, 2022 at 7:18 am

very clear explaination Please guide me how to rpeort these in APA stye

January 28, 2022 at 1:56 am

Thanks for your thorough reply. In that case, seems that there is no advantage for doing two tail test then? My understanding is the point for doing both side is you want to determine if H1 can be better or worse than H0. If excel did a one tail test in two ways at the same time, it seems that it can determine if H1 is better or worse at the same time.

This also leads to my 2nd question, why performing a both one-sided study at the same time is a NO-NO?

January 28, 2022 at 5:37 pm

If you perform both one-sided tests with a significance level of 0.05, that’s equivalent to a two-sided test with a significance level of 0.10. That test requires weaker evidence to be significant, which allows more false positives.

In some cases, that might be ok if the problems associated with a false positive aren’t too bad. However, when that’s the case, just perform the two-sided test with a significance level of 0.10!

I’ve written about when to perform one-tailed tests . That should answer your questions. Usually, when you have a choice to make about one- or two-tails, you’ll want to go with two-tails. However, there are exceptions. I go over that in that post!

January 27, 2022 at 4:49 am

Hi Jim, Quick question about one-tail. Can we choose the driection of the tail in excel? i.e. left or right tail? I wonder if excel has a default direction of the tail. Thanks, Rex

January 27, 2022 at 10:14 pm

Excel is a bit weird about this issue. You can’t specify one- or two-tailed tests. Nor can you specify the direction for one-tailed tests. But it ends up not being a problem, well, not completely at least!

Excel takes the first column and subtracts the second column from it to calculate the t-value. In the two-sample example, my t Stat is negative because the second column is larger than the first. However, Excel lists a positive critical value for this test. Hence, it should be impossible to detect a significant effect in a one-sided test as it looks like its testing the wrong direction. However, the one-sided results are, in fact, statistically significant. Excel must be taking the absolute value of the t Stat and comparing it to the critical value. In effect, this allows Excel to perform both one-sided tests at the same time, which is a NO-NO!

When you perform a one-tailed test, you should pick the direction before performing the test and then only perform the test in that one direction–not perform both like Excel.

At any rate, when using Excel, decide the direction first. Then, if you have significant results, just be sure it was in the direction you specified.

While you can’t specify the direction, it’s not really a problem other than that issue of performing both one-sided tests, which you can watch out for!

October 20, 2021 at 5:23 am

Hi Jim, thanks for the useful article. I would like to clarify on some matters.

I am running a paired samples t-test on the scores for 13 students who have gone through a school programme (before and after scores). My p value is 0.02 hence it is significant.

How do I get the effect size for the test? And am I right to say we only report effect size if the results are significant?

I also noticed there is a Pearson Correlation reported in the t-test table, stated as 0.796.. Would that be an effect size for my t-test?

Thanks very much!

October 20, 2021 at 4:07 pm

The effect size for a paired t-test is the difference between the means. In your case, that would be the mean of the 2nd test minus the mean of the first test. Typically, the null hypothesis value is that the mean difference is zero. Because your p-value is statistically significant, you can reject that null hypothesis and conclude that a non-zero effect exists in the population. The mean difference is the point estimate of the population value. You can also calculate a confidence interval to find a range of values that the population effect is likely to fall within.

Correlation is a different type of effect. In your case, it determines whether above average scores on the first test correspond with above average scores in the post test. That provides a different type of information than the mean difference. You can have a positive correlation between tests, like you do, even if there is no improvement between the 1st and 2nd test. There’s a p-value associated with Pearson’s correlation to determine whether it is significant.

You might be interested in my post about effect sizes .

I hope that helps!

October 13, 2021 at 2:27 pm

Hey Jim, I just bumped into your website and I have to say your arcticles are great – I definitely gonna follow you. I have one question regarding the t-test though:

I fully understand the one sample t-test where you divide the mean difference by the standard deviation of the sampling distribution where the latter is calculated by dividing the standard deviation of the sample by the square root of the sample size = s/SQRT(n).

BUT … when it comes to the two-sample test I don’t really get the hang off it – my question: why do you sum up the TWO standard deviations of the TWO samples in the denominator instead of taking the average?

I hope my question makes sense to … many thanks in advance!

October 13, 2021 at 4:59 pm

Hi Alexander,

Thanks so much for your kind words and welcome to my website! 🙂

There’s two forms of the t-test. One form uses the pooled standard deviation, which is essentially an average over the two samples. It’s really the standard deviation that combines the data for the two groups into one group. That works fine when the two groups have relatively equal variability. In fact, using the combined sample size to calculate the pooled standard deviation gives you a bit of extra statistical power to detect differences because you obtain a more precise estimate of the variability.

However, when the standard deviations for the groups are different, you shouldn’t average or combine them because the average or overall variability won’t accurately represent the variability of either group. In that case, you need to use each group’s standard deviation.

August 5, 2021 at 7:55 am

Hi Jim I appreciate your tutos. My question is, if I have to compare two programmes A1 and A2 on same input data, I used paired t-test and the output p-value is 1.1% assuming alpha=5%. That means A2 is better than A1. Now if I change the column order A2, A1, we get the same p-value, that means A1 is better than A2. I am confused here.

August 7, 2021 at 12:02 am

Paired t-tests analyze the differences between two columns. Consequently, if you change the order of one column, you’ll change the differences between the columns and, hence, the results. I’m guessing that’s what happened. You’ve reordered the columns in such a way that didn’t preserve the fact that each row needs to correspond to one item.

For more details about how paired t-tests work, read my post about t-tests .

July 10, 2021 at 1:39 am

Very descriptive and easy to understand. The examples really helped. Thanks Jim!

July 12, 2021 at 5:33 pm

You’re very welcome, Nathan! So glad it was helpful!

June 15, 2021 at 8:36 am

Jim, happy to see that you’ve added video to your content!

I hope you continue to add more of it 🙂

June 19, 2021 at 4:19 pm

Thanks so much for the positive feedback! I will keep adding more videos!

June 9, 2021 at 9:29 am

Hi Jim, I realised that when I conducted a t-test two sample assuming equal variance, my t-stat value is negative, what could that suggest. The question is linked to “ Is there statistical evidence that the average price of houses with one bathroom is different than the average price of houses with more than one bathroom?”

June 9, 2021 at 11:07 pm

That’s not a problem and the conclusion is still the same. When you’re performing a 2-sample t-test you are subtracting the mean of one group from another like this: Group A – Group B = Difference

If Group B has a higher mean than group A, you’ll obtain a negative value. I bet for your data, Group B is the set of homes with more than one bathroom, which presumably will have a higher mean price.

To learn more about the t-value, how it is calculated, and what it is for, read my post about T-values and T-distributions .

June 7, 2021 at 6:57 pm

I understand now. Thank you very much. I appreciate the support you have given me.

June 7, 2021 at 12:22 pm

Oh I did not know that.I would have to reject the null hypothesis as that value is less than the 5% significance level. Would that mean there is evidence that both averages are different? Based on the question about “Is there statistical evidence…” I stated in my previous comment, I also did a F-test Two Sample for Variances.Is that still fine to do based on the question.

June 7, 2021 at 4:44 pm

Yes, when you have a p-value less than your significance level, you reject the null hypothesis. That’s usually a good thing! Your p-value is lower than any reasonable significance level, which means that your results are statistically significant. To answer your question, it indicates that there is sufficient statistical evidence to conclude that the average housing prices between the two groups are different.

The Variance F-test answers a different type of question. It assesses whether the variability (rather than the mean) is different between the two groups. For more information, read my post about using Excel to analyze variability . If the question you’re answering specifically asks about the means prices, then you don’t want to use the Variances test because it’s answering a different question.

June 4, 2021 at 3:32 pm

Hi, I am seeking some assistance, A question states “Is there statistical evidence that the average price of houses with one bathroom is different than the average price of houses with more than one bathroom?”. There are 321 observations and I conducted a t-test two sample assuming equal and unequal variances. Both test shows a negative P value(two tailed) for example, was “1.3502E-14”. Is there a solution you could provide?

June 6, 2021 at 8:30 pm

There’s no such thing as a negative p-value. P-values range from 0 to 1. Oh, I see the problem. What you’re seeing is not a negative value, it is scientific notation, which is used for very large and very small numbers. In your case, it’s a very small number, which is good because it’s a p-value!

The number to the right of the E (-14) tells you how many places to move the decimal point. The negative value indicates you need to move the decimal point to the left by 14 places.

It’s equivalent to: 0.000000000000013502. In other words, it is very significant!

It sounds like you’re performing the correct analysis. You need to perform a 2-sample t-test (independent samples), two-tailed. Assess the variation to determine whether to use the equal or unequal variances.

May 29, 2021 at 9:25 pm

Hi Jim, I’m using 2-sample t-test, so your reply relieves my worry again. Now I can decide what to do with my samples with confidence. Thanks a lot! ^_^

May 29, 2021 at 9:26 pm

You’re very welcome! Glad to help!

Best of luck with your analysis!

May 29, 2021 at 9:00 pm

Thanks a lot, Jim. However, there’s another situation confusing me. Consider two groups of observations, each comprising of more than 50 observations, but the exact numbers of observations in each group are not equal. If t-Test is to be carried out in such situation, what kind of additional work should be done? Or that t Test isn’t capable of such situation at all? Thank you.

May 29, 2021 at 9:15 pm

2-sample t-tests with independent samples are perfectly able to handle unequal numbers of observations per group. Paired t-tests use dependent samples and assess the differences between paired observations, so the groups must be equal. I’m not sure which type of t-test you’re using.

If you’re using a paired t-test, you’ll need to remove the items/subjects that have only one observed value. Include only the items with two observations.

If you’re using 2-sample t-tests with independent samples, you don’t need to do anything when you have unequal sample sizes. The test performs fine with unequal sample sizes. However, if you’re planning to conduct a 2-sample t-test, you’d prefer to have equal sample sizes to maximize statistical power. For example, if your limited to 100 observations, you’d like to split those 50/50 to get the most power. If you split it 60/40, the test performs just fine without adjustments, but the power will be slightly less than if it was 50/50. But there is no requirement for equal sample sizes and there is nothing to do if you find yourself with unequal sample sizes.

May 29, 2021 at 6:22 am

Hi Jim, You tutorials help me a lot, thank you. As you explained, t-Test works well with samples comprising of 20 or more observations. However, I wonder would t-Test still be a good choice for samples comprising of more than one hundred observations? Or if there would be other fine technics suits such situation?

May 29, 2021 at 8:28 pm

I’m so glad to hear that they’ve been helpful!

No, there’s not really a better method with even more observations. Having more observations increases the statistical power of t-tests as well as increasing its robustness to departures from normality. Also, the width of the confidence interval will be narrower as you increase the sample size, which means you’ll have a more precise estimate of the effect size. So, you don’t need a different test for larger sample sizes, but larger samples improve the t-test and CI!

May 6, 2021 at 6:04 am

Hi Jim, i had a question about reporting the independent sample t-test results. I have reported the significance and reported the differences in the group means, But are we also suppose to report the assumptions as well? like “Levene’s Test of Equality of Error Variance showed a non-significant result, thus the assumption of homogeneity of variance was found and equal variances were assumed”? is this important to report ?

May 9, 2021 at 9:31 pm

Yes, that’s entirely acceptable. You can report the Levene test results as support for your decision to use equal variances.

Just as a caution, when you have a small sample size, it’s possible to obtain insignificant Levene’s test results even when the variances are not equal. That occurs because with a small sample size, Levene’s test loses statistical power (along with the t-test). Just something to be aware of with small sample sizes!

Also note that they don’t have to be exactly equal. Problems don’t start to occur until one variance is about twice the other.

May 3, 2021 at 1:25 pm

Adding onto this question I am also facing a similar issue – I’ve got 2 groups (n=50 & n=6) but the output on excel states df=7. I have used your calculation above and got an answer of 29. In this case is it still appropriate to be trusting excel? and if excel has got the df wrong, does this affect the t-stat or p-2-tailed value.

May 3, 2021 at 9:20 pm

Hi Rehana, it depends if you’re assuming equal variances or not. And, it depends on what the actual variances are too. Consequently, I can’t tell from what you write whether Excel calculated it correctly. The degrees of freedom definitely affect the t-value and p-value!

April 29, 2021 at 7:05 am

If I am comparing 2 variables with very different sample sizes (n=50 and n=6), should I do a test assuming unequal variances even if the variances are similar (0.058 vs 0.032)?

April 30, 2021 at 12:21 am

You don’t need to assume unequal variances because of the difference in sample size. However, one your variances is nearly double the other. That’s when you start to worry about the effect of unequal variances. Consequently, I would assume unequal variances for that reason.

April 15, 2021 at 4:47 pm

I am using this very helpful tool – thank you for your explanation. However, I am a bit worried about the degrees of freedom (df) outputted. I am conducting a 2-tailed t-test assuming unequal variances for 2 samples; “High” and “Low”. Each group has 130 observations included which I assume implies 258 degrees of freedom. However, the output I get for each test (20+ different pairs) varies as I have received df-values from everything between 223 a 258. Should I disregard this or? I believe you also show an output of a t-test of 2 groups, each with 15 observations, but with a df = 14….

Best regards, Oliver

April 15, 2021 at 4:56 pm

The df gets more complicated for unequal variances. The equation is below for a 2-sample t-test with unequal variances. But I would just trust the output!

April 13, 2021 at 1:43 pm

Hi, i am seeking some advise. I have done SEC on two differently treated samples, with 20 fractions analysed via NTA. so 20 fractions from method 1 and 20 fractions from method 2. I am wanting to compare the significant between a specific fractions and the different methods, So say fraction 6 from method 1 and fraction 6 from method 2. Is the correct thing to do is a one sample T test ? Thanks

April 15, 2021 at 4:27 pm

Hi Rose, if I understand correctly, you’re comparing two samples, so you’d need a 2-sample t-test. However, if you’re comparing multiple groups, you’ll need to use a post hoc test to control the family error rate .

April 5, 2021 at 4:44 pm

Yes, that data is just one column with years 2010-2019, and a second column labeled “Difference in Vehicle Accident Rates: Flip flops Minus All other Footwear”. Sorry my phone made it “slip shoes” in the last post and I didn’t notice. By range, I was just meaning the rate differences in the second column are from 3% to 9%. For more context, we are conducting a very basic multi-discipline case analysis. A lawsuit involving a vehicle collision where the plaintiff is suing the other driver, who was wearing flip flops. The plaintiff has obtained this survey data and there is a question as to whether it can be meaningfully analyzed to support a claim of negligence against plaintiff or possibly manufacturer. So the business law side is a separate component obviously, but I don’t see how to do hypothesis testing with this data. The assignment prompt is pretty vague with regards to statistical methods. The only thing I can think of is maybe regression analysis to show that more flip flop driving accidents are occurring each year, and therefore the manufacturer may need to consider product warnings to avoid strict liability.

Data is: YEAR/ ACC. DIFF

2010 4% 2011 5% 2012 3% 2012 4% 2014 6% 2015 5% 2016 7% 2017 8% 2018 7% 2019 9%

I can derive a mean, standard deviation, and t-stat, and probability from this info, but would they really be valid given how the data is presented to us?

April 5, 2021 at 9:22 pm

That’s a tricky dataset given the lack of details. We don’t know the raw counts and sample sizes for both groups for any of those years. If you had more details about the raw data, you might be able to determine whether flip flops had more accidents. This is really more of a proportions problem than a t-test problem. However, using a normal approximation, you might been able to do something along those lines with a t-test. However, proportions close to 0 or 100 are less likely to approximate a normal distribution. A large sample size can help, but we don’t know that either.

Also, whether you’re using a t-test or proportions test, the parameter in question (mean or proportion) has to remain constant over time. It doesn’t look like that’s the case.

It does appear that the difference between flip flop accidents and other shoes is increasing over the years. However, again, not knowing how the data were collected, sample sizes, locations, methodology etc., it’s not safe to draw those conclusions.

Finally, even if we could somehow trust the data and conclude the difference is growing, that still leaves us with a key unanswered question. Are flip flops causing the accidents? The data might be showing a correlation, but that doesn’t imply causation.

Of course, if the data are presented by the plaintiff, he’s not unbiased. We have no idea if the data are valid. Even if he didn’t intentionally bias the data, there are so many questions about the specifics, I’d say it’s impossible to draw any sound conclusions. If you trust the data, you might be able to say that the difference is growing over the years. But, without the raw data, you can’t apply any hypothesis test. Sample sizes might change over the years. Data collection locations might change. Etc. I’d imagine that if this was used in court, it would be ripped to shreds unless it was part of a published study. Any published study would have all those necessary details.

Maybe the point of the assignment is to know when not to use data because it can’t answer your question? Or, maybe you need to access the survey that you mention to verify all of those data and methodology questions?

April 3, 2021 at 7:41 pm

I just ordered your ebooks and I really appreciate you taking time to help others with their questions. Regarding determining the the appropriate statistical analysis, I am a bit stumped by a problem I am working on. For reference, I am a college sophomore studying business applications, so advanced analyses won’t apply. I am unsure how to approach a statistical analysis when very limited data is given. For example, I am provided only a table with two columns: years (n=10) and “difference in accident rates: slip shoes vs all other shoes”. I’m being asked to interpret the data. The rates are given as percentages, so I know the data is continuous; the range is (9-6=3), so I know there are no outliers and we can expect normal distribution. This seems like a paired t-test would be appropriate, except I’m confused because instead of being given separate data for slip shoes and all other shoes, I’m only given the differences. I’m not sure how I could model this in excel, although I have made basic calculations such as the standard deviation and mean. Any advice to point me in the right direction on analysis methods would be appreciated. Thanks for your time.

April 4, 2021 at 8:11 pm

I’d guess that the original data were binary, accidents versus no accidents. From that you’d get the proportions/percentages.

When you say the range is 3, I don’t know that’s referring to. Range of what?

Sounds like you need to do a 2-sample proportions test. Although, that would be hard to do without the underlying data. They’re showing you the difference in accident rates over time? And, you have one column for each year that is the difference in accidents as a percentage?

It’s hard for me to picture, so I’m having a difficult time coming up with recommendations. Do you think they’re asking you to look for trends over time?

April 1, 2021 at 10:00 pm

Hi Jim, thanks for the info. I might have a pretty straightforward question but can’t find any specific answer and was hoping you could help. I have a number of groups that I am comparing with a t-test assuming equal variances, due to the multiple comparisons a Bonferroni adjustment is required. Here I know for my data it is alpha/45 = 0.00111.

My question to you is, in using the data analysis package for the t-test in excel the alpha is 0.05 (no problem there) but it can be changed (here you stated that we should try and avoid changing it), when I do this with my 0.00111 bonferroni adjusted p-value the results are the same as with an an alpha of 0.05, so what is the point in changing alpha here? In addition, is it possible to do the bonferroni like this, or would it be better to two the t-test under alpha = 0.05 and then do the bonferroni adjustment ad hoc?

I look forward to hearing from you.

Kind regards, Dylan

April 1, 2021 at 11:36 pm

If you’re changing your alpha based on the Bonferroni correction, it makes sense that the rest of the results stay the same. That’s because the significance level is something that you as the researcher sets. It doesn’t enter into the calculations at all. To learn why, read my article about understanding significance levels . I’m not really sure why Excel asks for alpha.

What you need to do is compare the p-values to your alpha. With a lower alpha, the p-values must be lower to be significant. That can change which group comparisons end up being statistically significant. So, it is potentially changing the results in that sense. But, Excel’s output doesn’t change. And, when I say that an alpha of 0.05 is generally a good one to use, I mean for a single test and not multiple comparisons.

45 groups is a large number! As you’re seeing, that dramatically lowers your effective alpha for each comparison. Additionally, Bonferroni is a conservative procedure in that it plays it extra safe, but that causes you to lose statistical power.

Your case is one where Excel’s limitations might be a problem. I’d recommend reading my article about using post hoc tests . There are other comparison procedures that aren’t as conservative and if you don’t need to compare all pairwise groups, that can help too. I discuss all that in that other post. It might give you some ideas. On the other hand, if you’re happy with results you’re getting using Bonferroni for so many groups, it might not be a problem! I write about these post hoc tests in the context of ANOVA, but the ideas apply for multiple comparisons even if you don’t perform the ANOVA test.

I hope this helps!

March 11, 2021 at 11:07 am

I’ve collected electrical conductivity values of water samples at a site I’m doing a study at and would like to know which statistical test would be appropriate to use please. I have taken readings from 3 sites, and at 4 times in the day corresponding to low tide, high tide, and two intervening periods equally spread out within that. At each site and time of the day, I took two readings which I averaged to get a mean. I would like to know whether the values significantly differ at a certain site (where I have 4 mean readings), so I can say that the tidal impact on electrical conductivity is negligible because this test shows that at site 1, 2 and 3, they don’t vary significantly. I hope this makes sense!

February 22, 2021 at 3:41 pm

February 21, 2021 at 8:46 am

Hi Jim, Could you help me on my data interpretation for a t-test. I am confused to whether I have one or two tailed distributions and then whether my results are paired, homoscedastic or heteroscedastic data.

I am evaluating pollution change from 2019 to 2020 due to the covid lockdown. I have pollution data from the same time period 23/3 – 30/4 for 2019 and then 2020, for one pollution type for example nitrogen dioxide data.

Thank you in advance Cerys

February 22, 2021 at 3:18 pm

I have several posts about whether you should use a one or two-tailed test. I’d recommend reading those and then if you have more detailed questions, post them there. Typically, use a two-tailed test unless you have a strong rationale for using a one-tailed test. The posts describe what constitutes a “strong rationale.” One-tailed and Two-tailed Tests Explained When Can I Use One-Tailed Tests?

As for the equal variances assumption, you can do a formal variances test in Excel . However, if you have a very large sample size, the test will detect unsubstantial differences in variances that won’t affect your results. The general rule of thumb is to look at the standard deviations and if one is twice (or more) the size of the other, use Welch’s t-test which does not assume unequal variances. In fact, if you’re not sure, just use the unequal variances version and know that you can trust the results. You only lose a little bit of statistical power.

February 20, 2021 at 10:44 am

Excellent! Thank you, Jim. I will check out the pages suggested by you.

February 9, 2021 at 12:13 pm

Jim, please do check the question during your free time.

February 11, 2021 at 5:43 pm

Hi Prasanth, sorry for the delay. I’ve replied to your earlier comment!

February 8, 2021 at 5:08 am

Just got a quick questions cus im kinda confused right now. Im doing a study on The Effectiveness of Face-to-face Classes and Remote Learning among University Students. I honestly have no idea what to do nor what my Ho and Ha should be. I did a descriptive survey with questions about how effective is face to face learning and another set of questions for how effective is remote learning to gather data. I was wondering what the variables should be for x and y and what kind of test i should do cus im tasked to do a hypothesis testing on one population mean.

February 11, 2021 at 5:01 pm

It’s hard for me to answer some of your questions because I don’t know the type of data you are gathering. However, here’s one possibility.

If you measured effectiveness for in-person and remote classes using a continuous outcome variable, you could compare the mean effectiveness between the two groups. Your hypotheses would be the following:

H0: the mean in-person and remote effectiveness are equal. HA: the mean in-person and remote effectiveness are not equal.

You could then use a two-sample t-test to compare the means of the two groups. If your p-value is less than your significance level, you can reject H0 and conclude that the results are statistically significant. Significant results would indicate that your sample provides sufficient evidence to conclude the population means are different.

January 30, 2021 at 11:15 pm

sir if i have 2 groups one with diet and one with diet and yoga, and i want to compare their blood pressure and bmi before and after then what statitical tool should i use

January 31, 2021 at 2:15 am

Hi Remya, sounds like you need to use repeated measures ANOVA.

January 16, 2021 at 1:17 am

Thank you so much Jim for taking the time to reply to me.

Let me try to explain what I aming for. Let’s assume I have a group of 500,000 customers who hold a specific financial instrument. Out of those customers, 100,000 also hold another financial instrument. I want to test that the 100,000 customer group holding the 2 instruments (hence the uneveness of the two sample sizes), have a higher income than the rest 400,000 customers.

Is the Two-Sample t-Test the right test for this study?

Should I perform an F-test before proceeding with the Two-Sample t-Test?

Or, should I randomly select a 100,000 sample from customers with one instrument only to make the two groups even in sizes?

Thank you very much in advance Jim.

January 16, 2021 at 11:19 pm

Be sure that your groups are independent (different people, no people in both groups). For your scenario, you have 500,000 people. You’d split them so that the 100,000 with the other instrument are in one group and the people who don’t have it (400,000) are in the other. The 2-sample t-test is valid.

If you’re unsure about have equal variability between the groups, I actually would not recommend the F-test for such a large sample size. That test will have so much power with such a large sample size that even a trivial difference in variability will be statistically significant. Basically, a false alarm. Instead, just compare the standard deviations. If one standard deviation is twice (or more) the size of the other, then you have problems. And, if you’re unsure, you can perform the form of the t-test which doesn’t require equal variances.

Finally, your theoretically supposed to draw a random sample from a defined population. The results apply to the population that you drew the sample from. I’m guessing from what you wrote that is not how you acquired your data. Existing customers. You’ll have to evaluate how well it approximates a random sample from some population.

January 15, 2021 at 7:18 am

Hi Jim, this is extremely useful. Is there an issue with the reliability of the results when performing a Two-Sample t-Tests with uneven sample sizes? i.e. 87,000 vs 400,000. Thanks

January 16, 2021 at 12:46 am

It’s fine to use different sample sizes. The results are still valid. If you’re planning a study and want to maximize your statistical power, you’ll get the maximum power if you split the total sample size in half. For example, if you want a total sample size of 100, you’ll get maximum power for a 2-sample t-test if you split it 50/50 between the two groups.

However, if you’re working with an already uneven split, that’s no problem. And, with such large sample sizes that you have, you probably won’t have problems with low power.

There’s one potential snag you should think about. Why are the sample sizes so uneven? If it’s because subjects dropped out unevenly, you have the potential for selection bias if they dropped out in some manner related to the outcome. That’s not an issue with the hypothesis test but rather the underlying data.

January 14, 2021 at 9:23 pm

Hi Jim, Thank you for such handy explanations! I am a fairly amateur with stats, with just a single university unit under my belt. I am unsure how to approach the following data.

We have a collection of data with 4 different grocery store chains, measuring the brix (sugar level) and pressure of peaches form each shop over 8 weeks of the season. How do I go about testing the change data from the 4 different shops over 8 weeks. We would also like to see if there is a relationship between sugar and pressure, but I assume this is too many variables for a single test route, so would split into two tests.

Please note, my head is in a rabbit hole at this point, so I may be blind to some very obvious errors i’m making!

Hope you can help, cheers.

January 12, 2021 at 4:21 pm

Hello, Jim, first thank you for creating such a valuable resource. My question is this: Lets say I have three variables in each column: name, gender and height (in stacked column format, so i have one row of data for each unique value: e.g Dave, Male, 23 and then next column Sara, Female 31 and so on).

I want to compare if the mean height of men and women are statistically different in my data. How can i do that using excel. Can this be done? How?

Thank you and wishes for 2021.

January 14, 2021 at 2:16 pm

There are several things to consider about t-tests. For one thing, you can have at most only one categorical variable, such as gender. And, that categorical variable can only contain two groups. For gender, you can compare the mean of some characteristic between males and females. When using t-tests, you are not able to include a continuous variables, such as age.

It sounds like you need to use regression analysis, which will allow you to include both gender and age in the statistical analysis. Read my post about when to use regression analysis for more information. Also, I’ve written a post about how to perform regression analysis using Excel .

December 21, 2020 at 12:40 am

Great! Thanks so much for your help

December 20, 2020 at 8:07 pm

Hey Jim, thank you for the page – I have a doubt – Why can’t we use Confidence.t function in Excel to calculate margin of error for differences of two means of independent populations. As I understand, confidence.t function can be used to calculate margin of error for difference of two means for matched pair samples.

Hi Prasanth,

Sorry for the delay, I looked into the function. The Confidence.t function is designed to calculate the confidence interval for a single sample. In other words, you have one column of values and you want to calculate the CI for that column.

Yes, you can use this function to calculate the CI for the differences between paired samples. The reason this works is because a paired t-test is equivalent to a 1-sample t-test on the paired differences. The calculations are the same, which means it is ok to use the same functions.

However, it is correct that you CANNOT use it for the difference in means between independent groups. That’s because, unlike the example above, a 1-sample t-test is not equivalent to a 2-sample t-test. The calculations are different and you, therefore, can’t use the same commands.

For more details, look at my other post about t-tests and t-values , which illustrates the equivalence between paired t-tests and 1-sample t-tests. Note how 2-sample t-tests (independent groups) are different.

December 19, 2020 at 7:35 am

Hi Jim. Im getting a letter in my t-test result: 2.84997767514918E-06

What does this mean please?

December 19, 2020 at 7:03 pm

That is scientific notation, which is used for very large and very small numbers. In your case, it’s a very small number, which is good if it’s a p-value!

The number to the right of the E (-06) tells you how many places to move the decimal point. The negative value indicates you need to move the decimal point to the left by 6 places.

It’s equivalent to: 0.000002849. . . .

December 2, 2020 at 8:02 am

What do I put in the Hypothesized Mean Difference field though if the claim is that there is a difference in the means (the first one is greater than the second)?

December 3, 2020 at 1:55 am

Hi Kelly, I cover this in step 4 of the procedures.

December 1, 2020 at 9:47 pm

Hi Jim, I have a problem with my data that is i only have the sum of the samples without specific value for each sample, how should i perform it in excel? Can you help me, please? My data: 6241 worms / 30 samples. Thank you!

December 1, 2020 at 11:33 pm

You’d need to tell me more about the goal of your analysis. If you data comprise counts of worms for each sample, you might need to perform a Poisson rate test or Poisson regression. But, I’m not sure what you want to learn from your data.

November 9, 2020 at 12:30 pm

Hi Jim!!!! Thank you for the easy to understand explanation. I have used the t-test before but never truly understood it or when to use it the way I do now!

November 10, 2020 at 2:19 pm

Hi Stacey, thanks for writing! I’m so glad to hear that this post was helpful!

October 15, 2020 at 2:02 pm

Hi Jim. This is an excellent explanation. Thank you so much.

I was wondering if there is a way to calculate the standard deviation from the information that results from the excel output?

Thanks. Valodia

October 15, 2020 at 2:24 pm

Hi Valodia,

Yes, you can! The standard deviation is just the square root of the variance. So, all you need to do is look for variance in the output and take the square root of it to find the standard deviation. For example, if you look in Excel’s output for the 2-sample t-test, you’ll see variances for each group and the pooled variance. Take the square root of those variances to find the standard deviation for each group and the pooled standard deviation, which is for all the data.

September 3, 2020 at 3:50 am

Thanks for your comments Jim!! I ended up doing a 3 way ANOVA. I thought to share my results with you. Might interest you. Worked like a charm!!

September 3, 2020 at 10:29 pm

I’m so glad it worked like a charm!! I had to remove the output because it was so long. But, that’s great!

September 1, 2020 at 10:54 pm

Hi Jim, I am a newbie in discovering the wonders of Excel in calculations. I am in a dilemma now. I have this questionnaire with 4 dimensions – so lets say for Dimension A, I have 7 questions, Dimension B (11 questions), Dimension C (10 questions) and Dimension D (7 questions). Now I would like to see the relationship between gender and dimension as a whole. Now, my questions are (1) How do I group the 7 questions in Dimension A as one variable? (2) How do I group all the questions in the 4 dimensions as 1 variable? Thank you Jim.

August 27, 2020 at 3:24 am

Holy heck Jim no one has ever replied to me on any other stats message board. Thanks. I want to pick your brain a little bit more. Below will be the structure of the data. Such data will be collected for 4 weeks at two temperature each. Can i do a one factor anova for one week data and compare if the formulations are different? At the end of the study I will take each formulation at different weeks and do another anova to check how formulations behave over time. Is there a simpler way?

Week0 F1 F2 F3 F4 F5 F6 F7 F8 Outcome-Repeat1 x x x x x x x x Outcome-Repeat2 x x x x x x x x Outcome-Repeat3 x x x x x x x x

August 29, 2020 at 3:43 pm

I don’t see why you couldn’t do it that way. However, you’re throwing away valuable information by not including the week 0 (i.e. pretest) information. That would be my concern. If you have that data, you might as well use it in a repeated measures design.

If you go the route of a one-way ANOVA at week one, that would tell if the formulations have different group means at that point. However, that test will have less power than the repeated measures design including the pretest scores.

So, can it be done? Yes. It’s a simpler model too. But, you lose some statistical power. My upcoming post is about independent and dependent groups, which gets into repeated measures designs and the issues involved.

August 26, 2020 at 5:52 am

Hi Jim!! Big Fan. I have the following problem. Would like to know what you think

I am screening different liquid formulations for a drug. There are 8 unique formulations studied at two different temperatures. The aim is to identify which formulation is best with respect to stability. I was planning to do an ANOVA for the 8 different formulations to see if the formulations are different from each other and then do a pairwise t test at the two temperatures as it is essentially the same sample at two different temperatures. Is this a good approach?

August 26, 2020 at 3:34 pm

It sounds like you need to perform a repeated measures ANOVA, which is an extension of the paired t test. You’d include Formulation, Temperature, and Time in your model. You’d be particularly interested in the interactions between Formulation*Time (does the relationship between formulation and your DV depend on time). If that term is significant, then you know the formulations behave differently over time. Also, you could include temperature in that interaction. Three-way interactions are rare in analyses, but your model might call for it. It would answer the question whether the relationship between Formulation and the DV depends on both time and temperature.

Additionally, you can perform post hoc analyses on these interactions terms so you can control the familywise error rate for all these comparison. You don’t get that benefit if you use a series of paired t-tests.

July 28, 2020 at 9:08 am

how we will tell that mean of one variable is greater than the other variable in two tailed t test

July 29, 2020 at 12:27 am

There are two ways. One is just to look at the two mean values. The output should display the mean for each group. That’s the easiest way. For example, if the mean of group 1 is greater than group two, and the results are significant, then you know that the mean 1 is significantly larger than mean 2. You can also look at the t-values and then link that to critical areas and how that relates to your hypothesis test.

But, I’d just look at the group means!

July 10, 2020 at 5:03 am

Hi Jim. Could you please clarify if t-test can be used if data have a number of outliers, i.e. distributions are heavily skewed? Many thanks

July 12, 2020 at 5:47 pm

Hi Natalia,

T-tests are not recommended when you have outliers that skew the distributions. A few values can dramatically change your results. Consider using a nonparametric test instead. I talk about this in my post about parametric vs. nonparametric tests . It’s under one of the benefits of using nonparametric tests.

July 9, 2020 at 11:44 pm

I am trying to assess the impact of an intervention in a sample of 30 participants pre and post intervention using a set of clinical outcome measures sample before and after the intervention. As the intervention was applied at different times for each participant, I have collected different number of pre and post intervention outcome measures. To clarify: I have 2 pre-intervention outcome measures and only one post intervention measures for participant X while I have 3 pre-intervention and post intervention measures for participant Y.

What is the best way to do a paired sample T test in such a case? For some participants, there is no corresponding post-intervention outcome recorded as the prescriber did not request for the clinical test. Do I just choose the last recorded pre and post intervention values or do I calculate an average of pre and post intervention measures for each participant? If I am to calculate the average, then how do I account for a single or no outcome value?

I’d appreciate your advice on this.

July 9, 2020 at 5:27 am

Your website has been a heaven sent for me! My knowledge of statistics is rudimentary and I cannot thank you enough for the simple way in which you explain concepts. I am currently writing up my thesis and I had a question about selection of variables in a paired sample t-test. My research looks at the impact ( if any) of having a pharmacist involved in the care of people with type 2 diabetes and I have clinical parameters measured pre and post intervention. I also have a control group who have been exposed to usual care.

My question is – does it matter in what order I choose my sample variable when I do the paired t-test ( using XLSTAT). I have added pre-intervention means as variable 1 and post-intervention as variable 2. Would the values change if I conduct the test choosing post intervention as variable 1 and pre as variable 2?

Apologies in advance if this is a silly question but I just want to be sure I am on the right path here.

Once again, many thanks.

Cheers Geeta

July 7, 2020 at 12:52 pm

July 7, 2020 at 12:51 am

Hey sidra .you find the answer yet or not.i thnk tour student of b.ed i am also looking for ths answer

July 7, 2020 at 12:57 am

Hi, I answered Sidra’s question in the comments. You might need to scroll down.

July 5, 2020 at 6:41 pm

Thank you Jim. your answer was really helpful.

July 5, 2020 at 5:38 pm

Hi jim i am a new student of statistics. I really need to solve question. Can you give me any idea which test will be suitable for this research. “A researcher conducted a research that majority of the people who died during pandemic bought a new phone during last year.If his assumption is correct which statistical test should be appropriate “

July 5, 2020 at 5:49 pm

It sounds like you need to perform a two proportions test. You need to determine whether the proportion of pandemic deaths is different between the group that bought a new phone within a year versions those who did not. Caution: For these data, you wouldn’t be proving that buying phones caused pandemic deaths. Instead, you’d be showing a correlation (not causation) between them.

In my new ebook about hypothesis testing , I cover proportions tests in great detail.

July 3, 2020 at 8:01 am

I am conducting a quantitative research study. The hypothesis is does Variable 1 (intervention) impact Variable 2 (posttest scores). I have a control group and an experimental group and pretest and posttest scores for all participants. I have entered all scores as 1 data set in excel and from my understanding should use the paired mean t-test? But then how do I compare the groups? For example how do I find the mean for the experimental group separately from the control group? Or the separate standard deviations? When reporting results would I use the one p-value calculated for the total group for the null hypothesis testing?

July 3, 2020 at 5:06 pm

Based on what you write, it sounds like you’ll need to use repeated measures ANOVA rather than a t-test. You have pre-test and post-test scores and you have two experimental groups. That’s four groupings based on two independent variables, time and experimental group. T-tests can’t analyze that type of design.

To answer your question about whether the mean difference between pre-test and post-test scores depend on group assignment, you’ll need to include an interaction term for time*group in your repeated measures ANOVA model.

June 22, 2020 at 2:18 pm

I am attempting to run a t-Test comparing time to complete a task where the pre- and post array data each have 70 days. However, in the post data there are days when the task didn’t occur. Is the correct way to represent this within the array to include a 0 in each of the lines, or to leave it blank? I have run it each way and in this instance the p-value becomes >0.05 if the 0’s are included for time measures, but become significant if the 0’s aren’t included. However, I want to represent the data correctly regardless of the resulting significance.

Thank you for your help.

June 28, 2020 at 12:33 am

I’m assuming you’re using a pair t-test with a pre and post test time for each subject. In this case, if a subject doesn’t complete both the pre and post test, you need to exclude that subject from the analysis. You definitely don’t want to enter a zero for missing data because the analysis will interpret that as a time of zero.

I hope that helps. Best of of luck with your analysis!

May 20, 2020 at 6:31 am

Hi Jim! Your explanation helped me a lot to understand how a t-Test works! I have one question about multiple t-Tests. If I conducted a t-Test (4 groups 1,2,3,4 and tested 1&4 / 2&3) and ANOVA for all 4 together does this count as multiple Testing? Therefore a Bonferroni correction is needed? Thanks a lot!

May 21, 2020 at 10:41 am

Hi Kevin, yes, that definitely does count as multiple testing. You shouldn’t be using t-tests for that. Instead, conduct the ANOVA and then use a post hoc test . Click that link to read an article I wrote about them. Bonferroni is a type of post hoc test and that would be an acceptable one to use. However, I don’t usually recommend that particular test because it’s known to be more conservative (less statistical power) than other post hoc tests.

May 1, 2020 at 9:10 pm

Hi Jim (Great name btw!). I have a question about the one tailed P-Value that Excel reports. It specifically says “P[T <= t]" which if interpreted literally would be P[ T <= – 1.652] if the t test statistic were -1.652, and if there were, say, 13 df, then P[T <= -1.652] would be equal to 0.061235 (rounded off to the 5th decimal place). If, on the other hand, the t test statistic had been +1.652, then P[T <= 1.652] = 0.93877 (rounded off to the 5th decimal place), BUT Excel would still report, "P[T <= t"] as 0.061235. So my question is this: even though Excel prints "P[T <= t] one tailed", it's really calculating the left-hand tail probability if the test statistic is a negative number, and the right-hand tail probability if the test statistic is a positive number? Do I have that correct? Thanks (for the life of me I could not find the answer to this question out there on the "interwebs"!)

May 2, 2020 at 11:25 pm

Hi Jim! Yes, it is indeed a great name!

Those labels in Excel are wonky. My take is that they’re missing symbols for absolute values. I noticed this weirdness before but your comment prompted me to think it through for both one- and two-tailed cases. T are t-values in the t-distribution while t is from your sample.

For both one-tailed tests and two-tailed tests, I believe it should be: P(|T| ≥ |t|).

This indicates the probability of obtaining absolute t-values at least as extreme as the absolute value of the t-value your study obtained. That covers all the cases I think for both positive and negative sample t-values and one-tailed tests in either direction. Excel would include “one-tailed” or “two-tailed” after that label because that tells you whether that probability is distributed between two tails or just one tail.

–The other Jim

April 8, 2020 at 9:55 am

I would like to know if you happen to know which formula Excel are using when calculating the t-value in the unequal variance t-test? So sorry for my “danglish” 🙂

March 26, 2020 at 11:19 pm

Hi Jim, Super helpful, i need to create a box plot from my P value, how would i go about doing this. Thanks

March 29, 2020 at 3:20 am

Hi Lauren, I don’t understand what you mean by creating a box plot from a p-value. I do have a post that explains box plots . Perhaps that will help. If that post doesn’t answer your question, please comment in the boxplot post and provide more details about what you want to do exactly. Thanks!

December 13, 2019 at 11:35 am

Hi Jim. Thank you for the work you are doing here. I read this article to help me better explain how to decide which excel tool to use when for a t-test. It helped much! I was disappointed to learn that excel doesn’t do one sample t-tests… but maybe we have a ray of hope? I read another article you wrote about one or two or paired t-tests (first link in this article) and there you wrote “Here’s a secret! Paired t-tests and 1-sample t-tests are the same hypothesis test incognito!”

So, a question – can one, using excel’s paired t-test function, put sample data in the first column and the null hypothesis value, repeatedly, in the second column and “trick” excel into running a one sample t-test using the paired t-test function?

December 13, 2019 at 11:47 am

That’s a great question. If you enter your 1-sample data in one column and then enter all zeroes in the other column, that should do the trick. I haven’t tested that to know for sure but will try it when I can. I suppose there’s a chance that Excel might choke on that.

If that approach works, entering all zeroes for one column will test the hypothesis of whether your data are significantly different from zero. Entering a different value from zero should test whether your data are different from that value. For example, if you wanted to determine whether your data were significantly different from a mean of 5, you’d enter all 5s in the 2nd column.

As you’ve noted from the other post, paired t-tests simply subtract one observation from another for each subject/item and then performs a 1-sample t-test using those paired differences.

November 29, 2019 at 10:40 am

Thank you, Jim! Your explanation is very clear and it helps me a lot.

October 11, 2019 at 2:30 pm

Jim, I know you are an expert with Minitab. I recently learned that the test of two proportions have two ways to calculate the z score depending upon whether you indicate it use a pooled or separate estimate of p. Can you explain how I would decide that as the differences in results can be substantial. Also, I’m not sure which one is used in Excel.

September 29, 2019 at 5:50 am

A rather technical post but I’m sure this will be helpful for those mastering Excel.

September 29, 2019 at 1:54 pm

I do provide the step-by-step instructions for how to do this in Excel. However, I hope to have accomplished other goals in the process. For instance, there are several t-test to choose from–which one is appropriate. And, how to choose the correct p-value to use? What’s the correct interpretation? There’s also links to other resources of mine the explain the various other statistics you’ll see in the output. And, importantly, what can’t Excel do? Excel doesn’t elaborate on all of that. Hopefully, that information is useful to readers!

September 19, 2019 at 8:43 pm

Nice Explanation!!!!!!!!!!!

September 18, 2019 at 1:39 am

yess… it was so helpful.. thank you

Comments and Questions Cancel reply

The Complete Guide: Hypothesis Testing in Excel

In statistics, a hypothesis test is used to test some assumption about a population parameter .

There are many different types of hypothesis tests you can perform depending on the type of data you’re working with and the goal of your analysis.

This tutorial explains how to perform the following types of hypothesis tests in Excel:

• One sample t-test
• Two sample t-test
• Paired samples t-test
• One proportion z-test
• Two proportion z-test

Let’s jump in!

Example 1: One Sample t-test in Excel

A one sample t-test is used to test whether or not the mean of a population is equal to some value.

For example, suppose a botanist wants to know if the mean height of a certain species of plant is equal to 15 inches.

To test this, she collects a random sample of 12 plants and records each of their heights in inches.

She would write the hypotheses for this particular one sample t-test as follows:

• H 0 :  µ = 15
• H A :  µ ≠15

Refer to this tutorial for a step-by-step explanation of how to perform this hypothesis test in Excel.

Example 2: Two Sample t-test in Excel

A two sample t-test is used to test whether or not the means of two populations are equal.

For example, suppose researchers want to know whether or not two different species of plants have the same mean height.

To test this, they collect a random sample of 20 plants from each species and measure their heights.

The researchers would write the hypotheses for this particular two sample t-test as follows:

• H 0 :  µ 1 = µ 2
• H A :  µ 1 ≠ µ 2

Example 3: Paired Samples t-test in Excel

A paired samples t-test is used to compare the means of two samples when each observation in one sample can be paired with an observation in the other sample.

For example, suppose we want to know whether a certain study program significantly impacts student performance on a particular exam.

To test this, we have 20 students in a class take a pre-test. Then, we have each of the students participate in the study program for two weeks. Then, the students retake a post-test of similar difficulty.

We would write the hypotheses for this particular two sample t-test as follows:

• H 0 :  µ pre = µ post
• H A :  µ pre ≠ µ post

Example 4: One Proportion z-test in Excel

A  one proportion z-test  is used to compare an observed proportion to a theoretical one.

For example, suppose a phone company claims that 90% of its customers are satisfied with their service.

To test this claim, an independent researcher gathered a simple random sample of 200 customers and asked them if they are satisfied with their service.

• H 0 : p = 0.90
• H A : p ≠ 0.90

Example 5: Two Proportion z-test in Excel

A two proportion z-test is used to test for a difference between two population proportions.

For example, suppose a s uperintendent of a school district claims that the percentage of students who prefer chocolate milk over regular milk in school cafeterias is the same for school 1 and school 2.

To test this claim, an independent researcher obtains a simple random sample of 100 students from each school and surveys them about their preferences.

• H 0 : p 1 = p 2
• H A : p 1  ≠ p 2

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How to Do a T Test in Excel (2 Ways with Interpretation of Results)

Download the Practice Workbook

T Test.xlsx

T Test Types

There are two types of t-tests. They are:

• One-tailed t-test
• Two-tailed t-test

Each of them has 3 subtypes. They are:

• Two sample equal variance
• Two sample unequal variance

How to Do a T-Test in Excel: 2 Effective Ways

Method 1 – using the excel t.test or ttest function for a t-test, case 1.1 – two sample equal variance t-test.

In the dataset, you will see the prices of different laptops and smartphones. Here is a formula that performs a T-Test on the prices of these products and returns the t-test result.

=T.TEST(B5:B14,C5:C14,2,2)

We set the 3rd argument of the function to 2 as we are doing a two-tailed t-test on the dataset. The 4th argument should be 2 for a two-sample equal variance t-test.

Case 1.2 – Paired T-Test

We are going to apply another formula to calculate the Paired T-Test . The following dataset shows the performance mark of some employees in two different criteria.

=T.TEST(C5:C13,D5:D13,2,1)

Note: The explanation of the results is described in the following sections.

Method 2 – Using the Analysis ToolPak

• Go to the Options window.
• Select Add-ins and click on the Go button in the Manage section.

• The Add-ins window will appear. Select Analysis ToolPak and click OK again.

Case 2.1 – Tw-Sample Equal Variance T-Test

• Click on the Data Analysis button from the ribbon of the Data tab.
• The Data Analysis features will appear. Select t-Test: Two Sample Assuming Equal Variances and click OK .

• Set up the parameters for the t-test operation. Insert the Laptop and Smartphone prices as Variable 1 Range and Variable 2 Range. Include the headings in the range and check Labels.
• Set the value of Hypothesized Mean Difference to 0 .
• Select an Output option of your preference and click OK .

• As we have chosen a New Worksheet for the outputs, we will see the results in a new sheet.

Comments on Results

The output shows that the mean values for Laptops and Smartphones are 1608.85 and 1409.164 respectively. We can see from the Variances row that they are not precisely equal, but they are close enough to be assumed to have equal variances. The most relevant metric is the p-value .

The difference between means is statistically significant if the p-value is less than your significance level. Excel calculates p-values for one- and two-tailed T Tests .

One-tailed T Tests can detect only one direction of difference between means. A one-tailed test, for example, might only evaluate whether Smartphones have higher prices than Laptops . Two-tailed tests can reveal differences that are larger or smaller than. There are some other disadvantages to utilizing one-tailed testing, so I’ll continue with the conventional two-tailed results.

For our results, we’ll utilize P(T=t) two-tail, which is the p-value for the t-test’s two-tailed version. We cannot reject the null hypothesis because our p-value ( 0.095639932 ) is greater than the conventional significance level of 0.05 . The hypothesis that the population means differ is supported by our sample data. The mean price of Laptops is greater than the mean price of Smartphones’ .

The Analysis ToolPak also returns results for a one-tailed t-test . Here, the one-tailed P value of the two-sample equal variance t-test is 1.734 .

Case 2.2 – Paired T-Test

Select the t-Test: Paired Two Samples for Mean when you open the Data Analysis window.

The result shows that the mean for the Workpace is 104 and the mean for the Efficiency is 96.56 .

The difference between means is statistically significant if the p-value is less than your significance level. For our results, we’ll utilize P(T=t) two-tail, which is the p-value for the t-test’s two-tailed version. We cannot reject the null hypothesis because our p-value ( 0.188 ) is greater than the conventional significance level of 0.05 . The hypothesis that the population means differ is supported by our sample data. In particular, the Workpace mean exceeds the Efficiency mean.

How to Interpret T-Test Results in Excel

Let’s bring out the results again.

• The mean of laptop prices = 1608.85
• The mean of smartphone prices = 1409.164

ii. Variance

• The variance of laptop prices = 77622.597
• The variance of smartphone prices = 51313.7904

iii. Observations

The number of observations for both laptops and smartphones are 10 .

iv. Pooled Variance

The samples’ average variance, calculated by pooling the variances of each sample.

The mathematical formula for this parameter is:

((No of observations of Sample 1-1)*(Variance of Sample 1) + (No of observations of Sample 2-1)*(Variance of Sample 2))/(No of observations of Sample 1 + No of observations of Sample 2 – 2)

So it becomes: ((10-1)*77622.59676+(10-1)*51313.7904)/(10+10-2) = 64468.19358

v. Hypothesized Mean Difference

We “hypothesize” that the number is the difference between the two population means. In this situation, we chose 0 because we want to see if the difference between the means of the two populations is zero.

It indicates the value of the Degrees of Freedom. Formula for this parameter is:

No of observations of Sample 1 + No of observations of Sample 2 – 2 = 10 + 10 – 2 = 18

vii. t-Stat

The test statistic value of the t-Test operation.

The formula for this parameter is given below.

(Mean of Sample 1 – Mean of Sample 2)/(Square root of (Pooling Variance* (1/No of observations of Sample 1 + 1/No of observations of Sample 2)))

So it becomes: (1608.85 – 1409.164)/Sqrt(64468.19358 * (1/10 + 1/10)) = 1.758570846

viii. P(T<=t) two-tail

A two-tailed t-test’s p-value. This value can be found by entering t = 1.758570846 with 18 degrees of freedom into any T Score to P Value Calculator.

In this situation, the value of p is 0.095639932 . Because this is greater than 0.05 , we cannot reject the null hypothesis. This suggests that we lack adequate evidence to conclude that the two population means differ.

ix. t-Critical two-tail

This is the test’s crucial value. A t-Critical value Calculator with 18 degrees of freedom and a 95% confidence level can be used to calculate this number.

In this instance, the critical value is 2.10092204 . We cannot reject the null hypothesis because our test statistic t is less than this number. Again, we lack adequate information to conclude that the two population means are distinct.

Things to Remember

• Excel demands that your data be arranged in columns, with data from each group in a separate column. The first row should have labels or headers.
• Clearly state your null hypothesis (usually that there is no significant difference between the group means) and your alternative hypothesis (the opposite of the null hypothesis).
• As a result of the t-test, Excel returns the p-value. A small p-value (usually less than the specified alpha level) indicates that the null hypothesis may be rejected and that there is a substantial difference between the group means.

Frequently Asked Questions

Can I perform a t-test on unequal sample sizes in Excel?

Yes, you can use the T.TEST function to do a t-test on unequal sample sizes. When calculating the test statistic, Excel automatically accounts for unequal sample sizes.

What is the difference between a one-tailed and a two-tailed t-test?

A one-tailed t-test determines if the means of the two groups differ substantially in a given direction (e.g., greater or smaller). A two-tailed t-test looks for any significant difference, regardless of direction.

Can I calculate the effect size in Excel for t-tests?

While there is no built-in tool in Excel to calculate effect size, you can manually compute Cohen’s d for independent t-tests and paired sample correlations for paired t-tests using Excel’s basic mathematical operations.

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Hypothesis Testing Formula

Hypothesis Testing Formula (Table of Contents)

What is the hypothesis testing formula.

Before deep diving into hypothesis testing, we need to understand the hypothesis in the first place. In simple language, an idea is an educated and informed guess about anything around you, which can be tested by experiment or observation.

For example, A new mobile variant will be accepted by people; new medicine might work or not, etc. So a hypothesis test is a statistical tool for testing the hypothesis we will make and whether that statement is full or not. We select a sample from the data set and test a hypothesis statement by determining the likelihood that a sample statistics. So If your results from that test are not significant, it means that the hypothesis is not valid.

Formula For Hypothesis Testing:

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The z-test gives hypothesis testing. The formula for Z – Test is given as follows:

• X – Sample Mean
• U – Population Mean
• SD – Standard Deviation
• n – Sample size

But this is not as simple as it seems. To correctly perform the hypothesis test, you need to follow specific steps:

Step 1: First and foremost, to perform a hypothesis test, we must define the null and alternative hypotheses. An example of the null and alternate hypothesis is given by:

• H0 (null hypothesis): Mean value > 0
• For this, Alternate Hypothesis (Ha): Mean < 0

Step 2:  Next thing we have to do is that we need to find out the level of significance. Generally, its value is 0.05 or 0.01

Step 3:  Find the z-test value, also called test statistic, as stated in the above formula.

Step 4:  Find the z score from the z table given the significance level and mean .

Step 5:  Compare these two values, and if the test statistic is greater than the z score, reject the null hypothesis. You cannot reject the null hypothesis if the test statistic is less than the z score.

Examples of Hypothesis Testing Formula (With Excel Template)

Let’s take an example to understand the calculation of the Hypothesis Testing formula in a better manner.

Hypothesis Testing Formula – Example #1

Suppose you have been given the following parameters, and you have to find the Z value and state if you accept the null hypothesis or not:

Null hypothesis H0: Population Mean = 30

Alternate hypothesis Ha: Population Mean ≠ 30

Z – Test is calculated using the formula given below

Z = (X – U) / (SD / √n)

• Z – Test = ( 27 – 30 ) / (20 / SQRT(10))
• Z – Test = -0.474

Level of significance = 0.05

This is a Two tail test, so the probability lies on both sides of the distribution. So 0.025 on each side, and we will look at this value on the z table.

Source: https://www.z-table.com/

Since the significance level is 0.025 on each side, we need to find 0.025 in the z table. Once we see that value from the table, we must extract the z value.

If you see here, on the left side, the values of z are given, and in the top row, decimal places are given. So from that, we can say that 0.025 will give a z value of -1.96

So Z – Score = -1.96

We can reject the null hypothesis since the Z Test > Z Score.

Hypothesis Testing Formula – Example #2

Let’s say you are a school principal; you are claiming that the students in your school are above average intelligence. An analyst wants to double-check your claim and use hypothesis testing. He measures the IQ of all the students in the school and then takes a sample of 20 students. The following are the data points:

• Z – Test = (112 – 110)/ (15 / SQRT(20))
• Z – Test = 3.58

Null Hypothesis: Since population mean = 100,

• H0 : Mean = 100
• Ha: Mean > 100

Level of Significance = 0.05

Since the significance level is 0.05, we must find 1 – 0.05 = 0.95 in the z table. Once we find that value from the table, we must extract the z value.

Z – Table:

If you see here, on the left side, the values of z are given, and in the top row, decimal places are given. So from that, we can say that 0.95 lies between 1.64 to 1.65, mid-point of 1.645.

So Z Score = 1.645

Since the Z Test > Z Score, we can reject the null hypothesis and say students’ intelligence is above average.

Explanation

Everyone should remember that No hypothesis test is 100% correct, and there is always a chance of making an error. There is 2 type of errors that can arise in hypothesis testing: type I and type II.

Type 1: When the null hypothesis is true but rejected in the model. The level of significance gives the probability of this. So if the significance level is 0.05, there is a 5% chance that you will reject the true null.

Type 2: When the null hypothesis is not true but not rejected in the model. The probability of this is given the power of the test. Large sample size can help reduce the probability of this type of error, providing greater confidence in the model.

Relevance and Uses of Hypothesis Testing Formula

As discussed above, the hypothesis test helps the analyst test the statistical sample and, in the end, will either accept or reject the null hypothesis. The test assists in determining the accuracy of the formed hypothesis. If unexpected results occur, it may necessitate the formulation of a new hypothesis, which can then be tested. There are steps for any hypothesis test. The first step is to state the hypothesis, both the null and alternate hypothesis.

The next step is determining all the relevant parameters like mean, standard deviation , level of significance, etc., which helps determine  the z-test value . The third step determines the z score from the z table, and for this step, we need to see if it is a two-tail or single-tail test and accordingly extract the z score. The fourth and final step is to compare the results and then, based on that, either accept or reject the null hypothesis.

Hypothesis Testing Formula Calculator

You can use the following Hypothesis Testing Calculator

Z =

X − U = SD/√n 0-0 = 0 0/√0 Recommended Articles This has been a guide to Hypothesis Testing Formula. Here we discuss how to calculate Hypothesis Testing along with practical examples. We also provide a Hypothesis Testing calculator with a downloadable exceExcelplate. You may also look at the following articles to learn more – Examples of T Distribution Formula Calculator For Consumer Surplus Formula How To Calculate Equity Multiplier Formula Guide To Net Realizable Value Formula Altman Z Score (With Excel Template) *Please provide your correct email id. Login details for this Free course will be emailed to you By signing up, you agree to our Terms of Use and Privacy Policy . Download Hypothesis Testing Formula Excel Template Corporate Valuation, Investment Banking, Accounting, CFA Calculator & others Forgot Password? डाउनलोड Hypothesis Testing Formula Excel Template This website or its third-party tools use cookies, which are necessary to its functioning and required to achieve the purposes illustrated in the cookie policy. By closing this banner, scrolling this page, clicking a link or continuing to browse otherwise, you agree to our Privacy Policy Explore 1000+ varieties of Mock tests View more Submit Next Question 🚀 Limited Time Offer! - 🎁 ENROLL NOW Excel Tutorial: How To Test Hypothesis In Excel Introduction. Hypothesis testing is a crucial part of data analysis, helping us make informed decisions based on statistical evidence. It allows us to determine if there is enough evidence to support or reject a claim about a population parameter. In this Excel tutorial, we will delve into the process of testing hypothesis in Excel , providing you with the knowledge and skills to confidently analyze and draw conclusions from your data. So why is hypothesis testing so important? Well, it helps us make sense of the overwhelming amount of data we encounter in our professional and personal lives. Whether we are trying to understand consumer behavior, assess the effectiveness of a new product, or evaluate the impact of a marketing campaign, hypothesis testing allows us to make informed decisions and draw reliable conclusions. Key Takeaways Hypothesis testing is essential for making informed decisions based on statistical evidence. Understanding null and alternative hypotheses, as well as type I and type II errors, is crucial in hypothesis testing. Setting up and organizing data accurately in Excel is necessary for conducting hypothesis tests. Interpreting the results of a hypothesis test, including determining the p-value and significance level, is important for drawing reliable conclusions. Avoiding common mistakes such as misinterpreting results and using the wrong test for the data is vital in hypothesis testing. Understanding hypothesis testing Hypothesis testing is a crucial concept in statistics that allows us to make inferences about a population based on a sample. In the context of Excel, understanding hypothesis testing is essential for data analysis and decision-making. In hypothesis testing, the null hypothesis ( H0 ) is a statement that there is no effect or no difference in the population parameter. It is typically the hypothesis that researchers aim to refute. On the other hand, the alternative hypothesis ( Ha ) is a statement that there is an effect or a difference in the population parameter. It represents what the researchers are trying to prove. In hypothesis testing, there are two types of errors that can occur. A Type I error occurs when the null hypothesis is rejected when it is actually true. This is also known as a false positive. A Type II error occurs when the null hypothesis is not rejected when it is actually false. This is also known as a false negative. The significance level, often denoted as α , is the probability of rejecting the null hypothesis when it is true. In hypothesis testing, choosing the appropriate significance level is crucial as it determines the likelihood of making a Type I error. Commonly used significance levels include 0.05, 0.01, and 0.10. Setting up the data in Excel When conducting hypothesis testing in Excel, it is crucial to properly set up your data to ensure accurate results. Here are the key steps to follow: The first step in testing a hypothesis in Excel is to input your data into the spreadsheet. This may include numerical values, categorical data, or any other relevant information for your analysis. Once the data is inputted, it is important to organize it in a way that is conducive to hypothesis testing. This may involve structuring the data into relevant columns and rows, or creating separate sheets for different variables. Prior to conducting hypothesis testing, it is essential to ensure that the data is accurate and complete. This may involve checking for any missing or erroneous values, as well as verifying the overall integrity of the dataset. Input all relevant data into the Excel spreadsheet. Organize the data in a manner that facilitates hypothesis testing. Verify the accuracy and completeness of the data before proceeding with hypothesis testing. Performing a hypothesis test in Excel When it comes to testing hypotheses in Excel, there are a few key steps to follow to ensure accurate and reliable results. Here, we'll delve into the process of performing a hypothesis test in Excel, covering everything from selecting the appropriate test for the data to interpreting the test results. Before diving into the hypothesis testing process, it's crucial to determine the appropriate test for the data at hand. This involves understanding the nature of the data and the specific hypothesis being tested. Whether it's a t-test, chi-squared test, ANOVA, or another statistical test, choosing the right test is essential for obtaining meaningful results. Excel offers a range of built-in functions that make hypothesis testing relatively straightforward. Functions like T.TEST, CHISQ.TEST, and ANOVA help streamline the process, allowing users to input their data and quickly obtain test statistics and p-values. Understanding how to utilize these functions is key to executing hypothesis tests accurately. Once the hypothesis test has been run in Excel, it's important to carefully interpret the results. This involves analyzing the test statistic, p-value, and any relevant confidence intervals to determine whether the data provides enough evidence to support or reject the null hypothesis. Excel's output can provide valuable insights into the significance of the findings, helping to draw meaningful conclusions from the hypothesis test. Interpreting the results After conducting a hypothesis test in Excel, it is important to carefully interpret the results to draw meaningful conclusions. Understanding the p-value The p-value is a crucial indicator of the strength of evidence against the null hypothesis. A low p-value (typically less than 0.05) suggests that the results are statistically significant, and the null hypothesis can be rejected in favor of the alternative hypothesis. Significance level The significance level, often denoted as alpha (α), is the threshold at which the p-value is considered significant. Commonly used significance levels include 0.05 and 0.01. Rejecting or failing to reject the null hypothesis Based on the obtained p-value and significance level, it is possible to determine whether the null hypothesis should be rejected or retained. If the p-value is less than the significance level, the null hypothesis is typically rejected in favor of the alternative hypothesis. Considering the practical significance In addition to statistical significance, it is important to consider the practical implications of the results. Even if a hypothesis is statistically significant, it may not have meaningful real-world impact. Interpreting the findings in context It is essential to discuss the implications of the hypothesis test within the specific context of the research or analysis. This involves considering the broader implications and potential applications of the results. Considering limitations and alternative explanations Discussing the potential limitations of the hypothesis test and considering alternative explanations for the results can provide a more comprehensive understanding of the findings. Common mistakes to avoid When conducting hypothesis testing in Excel, it's important to be aware of common mistakes that can lead to inaccurate results. Here are some key pitfalls to watch out for: Misinterpreting the results of hypothesis tests is a common mistake that can lead to faulty conclusions. It's important to thoroughly understand the output of the test and consider the implications of the results before drawing any conclusions. Using the wrong hypothesis test for the type of data being analyzed can lead to incorrect results. It's essential to select the appropriate test based on the nature of the data and the research question being addressed. Failing to check for data integrity before conducting hypothesis tests can result in unreliable results. It's crucial to ensure that the data being analyzed is accurate and free from errors or anomalies that could impact the validity of the test. Recap: Hypothesis testing is a crucial step in data analysis as it allows us to make informed decisions based on the evidence provided by the data. Encouragement: I highly encourage you to apply the tutorial on hypothesis testing in Excel to your own data analysis projects. It's a valuable skill that can greatly enhance the quality and reliability of your conclusions. Final Thoughts: The significance of hypothesis testing in Excel cannot be understated. It is a powerful tool that enables us to make conclusions about the population based on sample data, ultimately leading to more accurate and meaningful insights. Immediate Download MAC & PC Compatible Free Email Support Related aticles The Benefits of Excel Dashboards for Data Analysts Unlock the Power of Real-Time Data Visualization with Excel Dashboards Unlocking the Potential of Excel's Data Dashboard Unleashing the Benefits of a Dashboard with Maximum Impact in Excel Exploring Data Easily and Securely: Essential Features for Excel Dashboards Unlock the Benefits of Real-Time Dashboard Updates in Excel Unleashing the Power of Excel Dashboards Understanding the Benefits and Challenges of Excel Dashboard Design and Development Leverage Your Data with Excel Dashboards Crafting the Perfect Dashboard for Excel An Introduction to Excel Dashboards How to Create an Effective Excel Dashboard Choosing a selection results in a full page refresh. Hypothesis Test in Excel for the Population Mean (Large Sample) Microsoft Excel for statistics > Hypothesis Test in Excel #1 Note : This article covers z-tests in Excel. If you have a small sample (under 30), or don’t know the population standard deviation , run a T Test in Excel instead. Hypothesis Test in Excel: Overview Hypothesis Test in Excel: Two Sample for Means Hypothesis test in excel: manual steps. Step 1: Type your data into a single column in Excel. For example, type your data into cells A1:A40. Step 2: Click the “Data” tab and then click “Data Analysis.” If you don’t see the Data Analysis button then you may need to load the Data Analysis Toolpak . Step 3: Click “ Descriptive Statistics “ and then click “OK.” When the Descriptive Statistics dialog box opens, click “Summary Statistics” and then type the location for a cell where you want your result to appear. For example, type”B1.” Step 4: Click “OK. ” A variety of descriptive statistics, like the median and mode , will appear starting in cell B1. Step 5: Find the cells that have the mean and the standard error results in it. If you typed in cell B1 in Step 3, your mean will be in cell C3 and your standard error will be in cell C4. Take a note of those cell locations. Step 6: Type the following formula into cell D1 (assuming your mean is in cell C3 and your SE is in cell C4 — if they are not, you’ll need to adjust the formula): (C3-0)/C4 Change the “zero” to reflect the mean in your null hypothesis . For example, if your null hypothesis states that the mean is $7 per hour, then change the 0 to “7.” Step 7: Press “Enter” to get the value of the test statistic. Compare the value to the accepted value for your mean from the z-table*. If the test statistic falls into the accepted range, then you will fail to reject the null hypothesis . Subscribe to our YouTube channel for more Microsoft Excel for Statistics help and tips! Comments are closed. The Complete Guide: Hypothesis Testing in Excel In statistics, a hypothesis test is used to test some assumption about a population parameter . There are many different types of hypothesis tests you can perform depending on the type of data you’re working with and the goal of your analysis. This tutorial explains how to perform the following types of hypothesis tests in Excel: One sample t-test Two sample t-test Paired samples t-test One proportion z-test Two proportion z-test Let’s jump in! Example 1: One Sample t-test in Excel A one sample t-test is used to test whether or not the mean of a population is equal to some value. For example, suppose a botanist wants to know if the mean height of a certain species of plant is equal to 15 inches. To test this, she collects a random sample of 12 plants and records each of their heights in inches. She would write the hypotheses for this particular one sample t-test as follows: H 0 :  µ = 15 H A :  µ ≠15 Refer to this tutorial for a step-by-step explanation of how to perform this hypothesis test in Excel. Example 2: Two Sample t-test in Excel A two sample t-test is used to test whether or not the means of two populations are equal. For example, suppose researchers want to know whether or not two different species of plants have the same mean height. To test this, they collect a random sample of 20 plants from each species and measure their heights. The researchers would write the hypotheses for this particular two sample t-test as follows: H 0 :  µ 1 = µ 2 H A :  µ 1 ≠ µ 2 Example 3: Paired Samples t-test in Excel A paired samples t-test is used to compare the means of two samples when each observation in one sample can be paired with an observation in the other sample. For example, suppose we want to know whether a certain study program significantly impacts student performance on a particular exam. To test this, we have 20 students in a class take a pre-test. Then, we have each of the students participate in the study program for two weeks. Then, the students retake a post-test of similar difficulty. We would write the hypotheses for this particular two sample t-test as follows: H 0 :  µ pre = µ post H A :  µ pre ≠ µ post Example 4: One Proportion z-test in Excel A  one proportion z-test  is used to compare an observed proportion to a theoretical one. For example, suppose a phone company claims that 90% of its customers are satisfied with their service. To test this claim, an independent researcher gathered a simple random sample of 200 customers and asked them if they are satisfied with their service. H 0 : p = 0.90 H A : p ≠ 0.90 Example 5: Two Proportion z-test in Excel A two proportion z-test is used to test for a difference between two population proportions. For example, suppose a s uperintendent of a school district claims that the percentage of students who prefer chocolate milk over regular milk in school cafeterias is the same for school 1 and school 2. To test this claim, an independent researcher obtains a simple random sample of 100 students from each school and surveys them about their preferences. H 0 : p 1 = p 2 H A : p 1  ≠ p 2 How to Change Axis Scales in Google Sheets Plots Statistics vs. analytics: what’s the difference, you may also like, how to create a stem-and-leaf plot in spss, how to create a correlation matrix in spss, how to convert date of birth to age in excel (with examples), excel: how to highlight entire row based on cell value, how to add target line to graph in excel, excel: how to use if function with negative numbers. #1 Excel tutorial on the net This example teaches you how to perform a t-Test in Excel . The t-Test is used to test the null hypothesis that the means of two populations are equal. Below you can find the study hours of 6 female students and 5 male students. To perform a t-Test, execute the following steps. 1. First, perform an F-Test to determine if the variances of the two populations are equal. This is not the case. 2. On the Data tab, in the Analysis group, click Data Analysis. Note: can't find the Data Analysis button? Click here to load the Analysis ToolPak add-in . 3. Select t-Test: Two-Sample Assuming Unequal Variances and click OK. 4. Click in the Variable 1 Range box and select the range A2:A7. 5. Click in the Variable 2 Range box and select the range B2:B6. 6. Click in the Hypothesized Mean Difference box and type 0 (H 0 : μ 1 - μ 2 = 0). 7. Click in the Output Range box and select cell E1. 8. Click OK. Conclusion: We do a two-tail test (inequality). lf t Stat < -t Critical two-tail or t Stat > t Critical two-tail, we reject the null hypothesis. This is not the case, -2.365 < 1.473 < 2.365. Therefore, we do not reject the null hypothesis. The observed difference between the sample means (33 - 24.8) is not convincing enough to say that the average number of study hours between female and male students differ significantly. Analysis ToolPak Learn more, it's easy Descriptive Statistics Moving Average Exponential Smoothing Correlation Download Excel File t-test.xlsx Next Chapter Create a Macro Follow Excel Easy Become an Excel Pro 300 Examples t-Test • © 2010-2024 Excel is Awesome, we'll show you: Introduction • Basics • Functions • Data Analysis • VBA Hypothesis Testing Calculator $H_o$: $H_a$: μ ≠   μ₀ $n$ =   $\bar{x}$ =   = $\text{Test Statistic: }$ = $\text{Degrees of Freedom: } $ $df$ = $ \text{Level of Significance: } $ $\alpha$ = Type II Error $H_o$: $\mu$ $H_a$: $\mu$ ≠   $\mu_0$ $n$ =   σ =   $\mu$ = $\text{Level of Significance: }$ $\alpha$ = The first step in hypothesis testing is to calculate the test statistic. The formula for the test statistic depends on whether the population standard deviation (σ) is known or unknown. If σ is known, our hypothesis test is known as a z test and we use the z distribution. If σ is unknown, our hypothesis test is known as a t test and we use the t distribution. Use of the t distribution relies on the degrees of freedom, which is equal to the sample size minus one. Furthermore, if the population standard deviation σ is unknown, the sample standard deviation s is used instead. To switch from σ known to σ unknown, click on $\boxed{\sigma}$ and select $\boxed{s}$ in the Hypothesis Testing Calculator. $\sigma$ Known $\sigma$ Unknown Test Statistic $ z = \dfrac{\bar{x}-\mu_0}{\sigma/\sqrt{{\color{Black} n}}} $ $ t = \dfrac{\bar{x}-\mu_0}{s/\sqrt{n}} $ Next, the test statistic is used to conduct the test using either the p-value approach or critical value approach. The particular steps taken in each approach largely depend on the form of the hypothesis test: lower tail, upper tail or two-tailed. The form can easily be identified by looking at the alternative hypothesis (H a ). If there is a less than sign in the alternative hypothesis then it is a lower tail test, greater than sign is an upper tail test and inequality is a two-tailed test. To switch from a lower tail test to an upper tail or two-tailed test, click on $\boxed{\geq}$ and select $\boxed{\leq}$ or $\boxed{=}$, respectively. Lower Tail Test Upper Tail Test Two-Tailed Test $H_0 \colon \mu \geq \mu_0$ $H_0 \colon \mu \leq \mu_0$ $H_0 \colon \mu = \mu_0$ $H_a \colon \mu $H_a \colon \mu \neq \mu_0$ In the p-value approach, the test statistic is used to calculate a p-value. If the test is a lower tail test, the p-value is the probability of getting a value for the test statistic at least as small as the value from the sample. If the test is an upper tail test, the p-value is the probability of getting a value for the test statistic at least as large as the value from the sample. In a two-tailed test, the p-value is the probability of getting a value for the test statistic at least as unlikely as the value from the sample. To test the hypothesis in the p-value approach, compare the p-value to the level of significance. If the p-value is less than or equal to the level of signifance, reject the null hypothesis. If the p-value is greater than the level of significance, do not reject the null hypothesis. This method remains unchanged regardless of whether it's a lower tail, upper tail or two-tailed test. To change the level of significance, click on $\boxed{.05}$. Note that if the test statistic is given, you can calculate the p-value from the test statistic by clicking on the switch symbol twice. In the critical value approach, the level of significance ($\alpha$) is used to calculate the critical value. In a lower tail test, the critical value is the value of the test statistic providing an area of $\alpha$ in the lower tail of the sampling distribution of the test statistic. In an upper tail test, the critical value is the value of the test statistic providing an area of $\alpha$ in the upper tail of the sampling distribution of the test statistic. In a two-tailed test, the critical values are the values of the test statistic providing areas of $\alpha / 2$ in the lower and upper tail of the sampling distribution of the test statistic. To test the hypothesis in the critical value approach, compare the critical value to the test statistic. Unlike the p-value approach, the method we use to decide whether to reject the null hypothesis depends on the form of the hypothesis test. In a lower tail test, if the test statistic is less than or equal to the critical value, reject the null hypothesis. In an upper tail test, if the test statistic is greater than or equal to the critical value, reject the null hypothesis. In a two-tailed test, if the test statistic is less than or equal the lower critical value or greater than or equal to the upper critical value, reject the null hypothesis. Lower Tail Test Upper Tail Test Two-Tailed Test If $z \leq -z_\alpha$, reject $H_0$. If $z \geq z_\alpha$, reject $H_0$. If $z \leq -z_{\alpha/2}$ or $z \geq z_{\alpha/2}$, reject $H_0$. If $t \leq -t_\alpha$, reject $H_0$. If $t \geq t_\alpha$, reject $H_0$. If $t \leq -t_{\alpha/2}$ or $t \geq t_{\alpha/2}$, reject $H_0$. When conducting a hypothesis test, there is always a chance that you come to the wrong conclusion. There are two types of errors you can make: Type I Error and Type II Error. A Type I Error is committed if you reject the null hypothesis when the null hypothesis is true. Ideally, we'd like to accept the null hypothesis when the null hypothesis is true. A Type II Error is committed if you accept the null hypothesis when the alternative hypothesis is true. Ideally, we'd like to reject the null hypothesis when the alternative hypothesis is true. Condition $H_0$ True $H_a$ True Conclusion Accept $H_0$ Correct Type II Error Reject $H_0$ Type I Error Correct Hypothesis testing is closely related to the statistical area of confidence intervals. If the hypothesized value of the population mean is outside of the confidence interval, we can reject the null hypothesis. Confidence intervals can be found using the Confidence Interval Calculator . The calculator on this page does hypothesis tests for one population mean. Sometimes we're interest in hypothesis tests about two population means. These can be solved using the Two Population Calculator . The probability of a Type II Error can be calculated by clicking on the link at the bottom of the page. How to Calculate P Value in Excel: Step-by-Step Guide (2024) P-value stands for probability value. It is used to define the statistical significance of any finding. You will see it being used for statistical hypothesis testing everywhere around you. Whether you are a statistician, an engineer, an accountant, or a scientist – you’ll have to use p-value for a variety of jobs. There are two easy ways to calculate p-value in Excel. 😀 Download our sample workbook here and follow the guide till the end to learn them both. Table of Contents What is the p-value? Measuring the P-value How to calculate p-value with Analysis Toolpak Decoding the p-value Find the p-value with the T-TEST function One-tailed p-value Two-tailed p-value What is a p-value? P-Value is used to perform hypothesis testing. It indicates how statistically significant a value might be. The p-value tells if a null hypothesis is valid or not. It can be measured in terms of its significance ranging from 0 to 1 . The lower the p-value, the greater the chances of invalidity of the null hypothesis . (This means there are more chances for the alternative hypothesis to be true) Higher the p-value, the lesser the chance of invalidity of the null hypothesis. The measure of the p-value: P-value can be somewhere between 0 to 1. You might express it as a decimal, for example, 0.5. Alternatively, you might express it as a percentage i.e. 50%. The significance of a p-value can be measured on three levels. Significant : Equal to or less than 5% (0.05) Marginally Significant : Between 0.05 (5%) to 0.10 (10%) Insignificant : More than 0.10 (10%) The validity of the null hypothesis depends upon the p-value. A null hypothesis for the comparison of two things indicates that there is no relation between them. An alternative hypothesis suggests otherwise. How to calculate p-value with Analysis ToolPak There are two main ways how you can find p-value in Excel. Out of these two, the first one is through the T.test tool in Analysis ToolPak. Let’s begin learning. The image below shows the actual and expected scores for some students. It’s time we compute the p-value for this data! 🤨 Begin with adding ‘Analysis ToolPak’ to your Excel. 1. Go to File > Options > Add-ins . 2. Check the option for Excel Add-ins at the bottom. 3. Check ‘Analysis ToolPak’ from the next window and hit ‘Ok’. And there you are! 4. Reach out for the ‘Data’ tab on the Ribbon to see something new added. 5. Go to Data > Data Analysis. This will launch the Data Analysis tool kit, as shown below. 6. Select ‘t-Test: Paired Two Sample for Means’ and click ‘Okay’ . In the ‘t-Test: Paired Two Sample for Means’ dialogue box that opens up, make the right inputs. 7. Select the first data set (Actual scores in this case) as Variable Range 1 . 8. Select the second data set (Expected scores in this case) as Variable Range 2 . 9. As our data consists of labels (headers), check the ‘Labels’  option. Don’t miss out on the Alpha value. An appropriate measure of the Alpha value is 0.05. 10. Define the output range – where do you want the t-tail test’s results to appear? We have created a reference to Cell E1 as the Output Range in this very instance. You are all good to go now! Press ‘Ok’ to have the following results. Excel gives a whole table with many statistical results in addition to the one-tail and two-tail p-value. Don’t want the p-value in terms of decimals but percentage? Select both the p-values and go to Home > Format > %. Adjust the decimal number to your choice. Decoding the P-value: Performing the t-test to find the p-value might not be as difficult as is to decode the obtained results. Let’s make it easy for you. The one-tail test gives a p-value of 0.383 (38.3%), which is more than 0.10 (10%). This means the p-value indicates that there is little evidence against the null hypothesis being invalid. The null hypothesis in this case holds true. Simply put, the ‘Null hypothesis’ of there being a relation between the actual scores and the expected scores stands valid (and the alternative hypothesis fails). The two-tailed test gives a p-value of 0.766 (76.6%), which is much more than the p-value above and 0.10 (10%). This serves as strong evidence that the null hypothesis is true. If you only need the p-values, you need not follow the process above. Instead, you can instantly find the p-values for any given data set by using the TEST function. 😄   One-Tailed P-Value: 1. Write the TEST function as follows: The first argument (array_1) refers to the range that contains the first variable (actual scores). 2. Create a reference to the second variable (expected scores) as array_2 . =T.TEST (B1:B8, C1:C8, 3. Set the tails argument as 1. =T.TEST (B1:B8, C1:C8, 1 It refers to the number of distribution tails. We have set it to 1 because we are finding a one-tailed p-value. 4. The t-test type argument can be set to three modes. 1 = paired. 2 = two-sample equal variance (homoscedastic) 3 = two-sample unequal variance (heteroscedastic) We are setting it to ‘1’. =T.TEST (B1:B8, C1:C8, 1, 1) All done! Hit enter to have the one-tailed p-value for this data set generated.   Two-Tailed P-Value: Can you generate a two-tailed P value in Excel using the T.Test function? Yes, see below. 1. Write the following formula: =T.TEST (B1:B8, C1:C8, 2, 1) Set the tails argument to ‘2’ instead of ‘1’. Everything else remains the same. Go ahead and hit ‘Enter’. And there you go! Computing p-value using the simple formula of T.Test is more like a child’s play. Did you notice how both methods yield the same result? Both the one-tailed and two-tailed p-values stand tallied. Which method to use? 🤔That depends upon your needs. If you are only in need of the P-value, save time by using the T.Test function. However, to fetch other details in respect of your dataset (Mean, Variables, etc.) too, better go with the T-Test tool in the Analysis ToolPak. That’s it – Now what? That’s all about finding p-values in Excel. We not only learned two different ways of finding p-values but also two different types of p-values – one-tailed and two-tailed. However, that doesn’t even make an iota of the number of functions offered by Excel. Excel offers a wide variety of other useful functions that you’ll have to use time and time again. Most importantly, the VLOOKUP, SUMIF, and IF functions. Register for my free 30-minute email course to learn these functions (and more!). Other relevant resources: You might have to use the p-value function in pair with other statistical functions. Do take a look at how to Calculate the Correlation Coefficient and Confidence Interval in Excel to manage all your statistical projects single-handedly. How to Find Null and Alternative Hypothesis in Excel Introduction. In the realm of statistical analysis, hypotheses play a pivotal role in testing theories and making inferences about populations based on sample data. Microsoft Excel, a widely used tool for data analysis, offers functionalities that can assist researchers and data analysts in setting up and testing hypotheses. This article delves into the intricacies of identifying null and alternative hypotheses within Excel, providing a comprehensive guide for professionals and enthusiasts alike. We will explore the theoretical underpinnings, practical applications, and step-by-step procedures to harness Excel’s capabilities for hypothesis testing. Understanding Hypothesis Testing What is a null hypothesis, what is an alternative hypothesis. Conversely, the alternative hypothesis ( Ha ) suggests that there is an effect or a difference. It is what the researcher wants to prove or is suspecting to be the case. The Role of Hypothesis Testing in Excel Setting up hypotheses in excel, formulating hypotheses. Before jumping into Excel, it’s crucial to clearly define your null and alternative hypotheses. This is typically done based on the research question or the problem statement. Example of Hypothesis Formulation Let’s consider a case study where a company wants to test if a new training program has improved employee productivity. The null hypothesis would be that the training has no effect on productivity ( H0: μ = μ0 ), while the alternative hypothesis might be that the training has increased productivity ( Ha: μ > μ0 ). Excel Tools for Hypothesis Testing Data analysis toolpak. To test hypotheses in Excel, you can use the Data Analysis ToolPak. This add-in provides a range of statistical tests, including t-tests, z-tests, ANOVA, and regression analysis. Installing the Data Analysis ToolPak Conducting hypothesis testing in excel, using the t-test for means. A t-test is used when comparing the means of two groups. In Excel, you can perform a t-test by selecting ‘Data Analysis’ and then ‘t-Test’. Example of a t-Test in Excel Using our case study, we can conduct a paired t-test if we have productivity data before and after the training for the same employees. Input the data ranges for both sets and define the hypothesized mean difference (usually 0 if testing for no change). Interpreting the Results Advanced hypothesis testing techniques, anova for multiple groups. When comparing means across more than two groups, ANOVA (Analysis of Variance) is the appropriate test. Excel’s Data Analysis ToolPak also offers this functionality. Regression Analysis for Predictive Modeling Regression analysis is used to model the relationship between a dependent variable and one or more independent variables. This can also be used for hypothesis testing regarding the slope of the regression line. Visualizing Hypothesis Tests Creating charts and graphs, common pitfalls and best practices, avoiding type i and type ii errors. A Type I error occurs when the null hypothesis is wrongly rejected, while a Type II error occurs when the null hypothesis is wrongly accepted. Understanding these errors and setting an appropriate significance level can mitigate their risks. Ensuring Data Validity The accuracy of hypothesis testing is contingent on the quality of the data. Ensuring data validity and reliability is paramount before conducting any statistical tests. FAQ Section Can excel perform all types of hypothesis tests, how do i choose the right statistical test for my data. The choice of statistical test depends on the type of data you have and the nature of your research question. Factors to consider include the level of measurement of your data, the distribution of your data, and whether your samples are independent or paired. What is the significance level in hypothesis testing? The significance level, often denoted by alpha (α), is the probability of rejecting the null hypothesis when it is actually true. A common choice for α is 0.05, but it can be set lower or higher depending on the context of the research. For further reading and to deepen your understanding of hypothesis testing in Excel, consider exploring the following resources: Excel Formula If Date Is Greater than Another Date You’ve entered too few arguments for this function, how to create a google sheet to share and edit. Your email address will not be published. Required fields are marked * Lean Six Sigma & SPC Excel Add-in Questions? Contact Us 888-468-1537 Statistical Analysis in QI Macros Statistics wizard, data normality, hypothesis tests, test of means, equivalence tests, test of variances, test for outliers, test of proportion, test relationship, data transformation, non-parametric tests. 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Qi macros ready-made template will calculate sample size for you. QI Macros  Sample Size Calculator  works with both variable (measured) and attribute (counted) data. Calculate Sample Size using QI Macros Click on QI Macros menu > Find QI Macros Tools > Sample QI Macros will pull up the Sample Size Template for you. Adjust yellow shaded cells to get the sample size. How to Calculate Sample Size using QI Macros template QI Macros adds a new tab to your Excel menu. To open the sample size template just click on QI Macros menu > Calculators > Sample Size Calculator. You should see the following: To calculate a sample size you need to know: The confidence level required (90%, 95%, 99%) α = 0.1, 0.05 , 0.01 (Type I Error) The Power required (80%, 85%, 90%) β = 0.2, 0.15, 0.1 (Type II Error) The desired width of the confidence interval δ - Maximum allowable error of the estimate = 1/2 * tolerance σ - estimated standard deviation (high value - low value)/6. To prevent calculation problems, QI Macros uses a default value = 0.167 (1/6). The defaults are set to standard parameters, but can be changed. Confidence Level In sampling, you want to know how well a sample reflects the total population. The α = 0.05 - 95% confidence level means you can be 95% certain that the sample reflects the population within the confidence interval. Step 1 - Choose alpha α = 0.05 - 95% Confidence Level Step 2 - Choose beta β = 0.1 - 90% Power Confidence Interval The confidence interval represents the range of values which includes the true value of the population parameter being measured. Step 3 - Set the confidence interval to half the tolerance or maximum allowable error of the estimate. (e.g., + 0.05, 2, etc.) Step 4 - Attribute data (pass/fail, etc.) - Set percent defects to 0.5 If 95 out of 100 are good and only 5 are bad, then you wouldn't need a very large sample to estimate the population. If 50 are bad and 50 are good, you'd need a much larger sample to achieve the desired confidence level. Since you don't know beforehand how many are good or bad, you can set the attribute field to (50% or 0.5). Step 5 - Variable Data - Enter Standard Deviation If you know the standard deviation of your data (from past studies), then you can use the standard deviation. If you know the specification tolerance, then you can use (maximum value - minimum value)/6 as your standard deviation. (The default is 1/6 = 0.167). Step 6 - Enter the total population (if known) Using the default values (95%, + 0.05, Stdev = 0.167) Step 7- Read the Sample Size Use the sample size calculated for your type of data: Attribute or Variable. Please Note : Our Sampling Tables are identical to that of the ANSI Z1.4 tables. QI Macros offers general inspection levels, however not special inspection levels. Attribute Sample Size Calculator Example Attribute Sample Size:  What if you were using attribute data, (e.g., counting the number of defective coins in a vat at the Denver Mint) but didn't know how many coins were in the vat? You'd need 384 coins to be 95% confident that the coins fell within the 5% interval. What if you knew there were 1000 coins in the vat (population known)? You only need 278 to be confident. What if you changed the confidence interval to be + 0.1? You only need 88 to be 95% confident. Variable Sample Size Calculator Example A sample must be selected to estimate the mean length of a part in a population. Almost all production falls between 2.009 and 2.027 inches. Estimated standard deviation = (2.027 - 2.009) / 6 = 0.003. And you want to be 95% confident that the sample is within +/- 0.001 of the true mean. Enter the data as shown below: You need 35 samples using α alone and 95 using α and β together. The Sample Size Calculator is one of many tools included in QI Macros add-in for Excel. QI Macros adds a new tab to Excel's menu, making it easy to find any tool you need. If you can't locate a tool, use the find tools feature on the far right side of QI Macros menu. QI Macros draws these SPC charts as well SPC Software for Excel Free 30 Day Trial On-line Tech Support QI Macros Reviews Free QI Macros Training Privacy Policy KnowWare International, Inc. 2696 S. Colorado Blvd., Ste. 555 Denver, CO 80222 USA Toll-Free: 1-888-468-1537 Local: (303) 756-9144 194 IMAGES Hypothesis Testing Formula Hypothesis Testing Formula Hypothesis Tests Hypothesis Testing Formula Hypothesis Testing Formula Hypothesis Testing Population Proportion COMMENTS The Complete Guide: Hypothesis Testing in Excel This tutorial explains how to perform hypothesis testing in Excel, including several examples. How to do t-Tests in Excel Step-by-step instructions for using Excel to run t-tests. Learn how to select the correct t-test, choose the right options, and interpret the results. The Complete Guide: Hypothesis Testing in Excel In statistics, a hypothesis test is used to test some assumption about a population parameter. There are many different types of hypothesis tests you can perform depending on the type of data you're working with and the goal of your analysis. This tutorial explains how to perform the following types of hypothesis tests in Excel: One sample t ... How to Do a T Test in Excel (2 Ways with Interpretation of Results) The article shows two easy ways of how to do a t test in Excel. The T.TEST function and Data Analysis Toolpak are used to operate the t test. Hypothesis Testing Formula Guide to Hypothesis Testing Formula. Here we will learn how to calculate Hypothesis Testing with examples, Calculator and downloadable excel template. Excel Tutorial: How To Test Hypothesis In Excel Introduction Hypothesis testing is a crucial part of data analysis, helping us make informed decisions based on statistical evidence. It allows us to determine if there is enough evidence to support or reject a claim about a population parameter. In this Excel tutorial, we will delve into the process of testing hypothesis in Excel, providing you with the knowledge and skills to confidently ... Hypothesis Test in Excel for the Population Mean (Large Sample) Step-by-step directions for a hypothesis test in Excel . Hundreds of how-to articles on statistics and probability. Free homework help forum, calculators. Hypothesis t-test for One Sample Mean using Excel's Data Analysis This video shows how to conduct a one-sample hypothesis t-test for the mean in Microsoft Excel using the built-in Data Analysis (from raw data).How to load ... The Complete Guide: Hypothesis Testing in Excel This tutorial explains how to perform hypothesis testing in Excel, including several examples. Hypothesis Testing in Excel: A Practical Handbook Hypothesis testing is a crucial statistical method used to draw meaningful conclusions about populations based on sample data. Excel, a ubiquitous spreadsheet tool, can be a handy companion in ... t-Test in Excel (In Easy Steps) This example teaches you how to perform a t-Test in Excel. The t-Test is used to test the null hypothesis that the means of two populations are equal. Hypothesis Testing Calculator with Steps Hypothesis Testing Calculator. The first step in hypothesis testing is to calculate the test statistic. The formula for the test statistic depends on whether the population standard deviation (σ) is known or unknown. If σ is known, our hypothesis test is known as a z test and we use the z distribution. If σ is unknown, our hypothesis test is ... One-Sample t-Test Describes the one-sample t-test and how to carry it out in Excel. Includes assumptions, confidence intervals, power, and sample size requirements. 6.2 Hypothesis Testing Learn how to use Excel to perform hypothesis testing for a single population mean. This chapter explains the steps and formulas with examples and exercises. How to Conduct a Two Sample t-Test in Excel A simple explanation of how to conduct a two sample t-test in Excel, complete with an example that shows the exact steps to follow. How to Calculate P Value in Excel: Step-by-Step Guide (2024) How to calculate p-value with Analysis ToolPak There are two main ways how you can find p-value in Excel. Out of these two, the first one is through the T.test tool in Analysis ToolPak. How to Find Null and Alternative Hypothesis in Excel The Role of Hypothesis Testing in Excel Excel does not directly create hypotheses for you; instead, it provides tools to test them. The process involves identifying the null and alternative hypotheses based on the research question and then using Excel's statistical functions to conduct the test. How to Conduct a Paired Samples t-Test in Excel A simple explanation of how to conduct a paired samples t-test in Excel, including an example that shows the exact steps to follow. Paired Sample t-Test Paired Sample t-Test Basic Concepts In paired sample hypothesis testing, a sample from the population is chosen and two measurements for each element in the sample are taken. Each set of measurements is considered a sample. Unlike in two-sample hypothesis testing (see Two-sample t-Test ), the two samples are not independent of one another. Paired samples are also called matched samples or ... Hypothesis Testing Excel Struggling with hypothesis testing? This Excel add-in can choose the right test, make the calculations and interpret the results for you. Try free for 30 days. Null & Alternative Hypothesis The typical approach for testing a null hypothesis is to select a statistic based on a sample of fixed size, calculate the value of the statistic for the sample, and then reject the null hypothesis if and only if the statistic falls in the critical region. Sample Size Calculator How to Calculate Sample Size using QI Macros template QI Macros adds a new tab to your Excel menu. To open the sample size template just click on QI Macros menu > Calculators > Sample Size Calculator. You should see the following: The recent Iranian election: Should we be suspicious that the vote Seems like the philosophical problem is that they want to use the same data to both generate the hypothesis and to test it. I am interested to know what theory of vote tampering would result in divisibility by 3. The tests I'm aware of are based on digit frequency (Benford's Law, for example), and have a sound philosophical basis. Power and Sample Size Calculate the power of a test (post hoc): After conducting a specific statistical test with a selected alpha and sample size, you can determine the effect size, and then calculate the power of the test. essay homework paper phd project resume review speech thesis