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How to Write a Laboratory Procedure Using Flow Chart Method

How to Understand & Create Simple Flow Charts of Algorithms

Because laboratory procedures tend to be an organized sequence of steps, with expected outcomes, the process can be represented with a flow chart. Using a flow chart makes it easy to follow the flow of the procedure, tracing it through the different outcomes, each to the proper ending. Because all laboratory procedures have different steps and different points in which multiple outcomes are possible, there isn't a single flow chart to represent all procedures. Constructing an appropriate flowchart, however, is an easy matter of putting together the correct symbols for each step involved. All you need to know is the proper symbol usage.

Draw a parallelogram to indicate a step in the procedure which requires input, such as material to be tested, or will produce output, such as a mixture.

Connect each box, which represents an aspect of the procedure, to the following box (or step) with a line. If you like, you can add arrows to the line to show the direction of flow, although typically, a flow chart flows naturally from the top down to the bottom. When you have lateral (or sideways) movement, however, you might wish to add arrows for clarity.

Use a basic rectangle for each of your straightforward processing steps in which there is only one outcome that will lead to the next step.

Connect to a diamond box when the step can produce more than one result. Perhaps this is a testing phase in your laboratory procedure in which the sample might test as positive or negative. (Or you might be testing for different ranges of values). For each outcome, draw a line from this diamond box to start a new branch of your flowchart. Label each branch with the outcome result, such as "positive" and "negative."

Use a round circle to represent the stop or end of a procedure step. Perhaps after a result comes up negative, there is no further testing. In this situation, the line would lead to an end circle.

Connect together parallelograms, rectangles, diamonds and circles in whatever order your procedure dictates until all paths either end at a circle or the path points back up to a previous step for situations where you must go back and repeat the procedure. Once all outcomes are covered, your laboratory procedure is completed.

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Scientific Method Flow Chart

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 These are the steps of  the scientific method  in the form of a flow chart. You can download or print the flow chart for reference. This graphic is available for use as a PDF image .

The Scientific Method

The scientific method is a system of exploring the world around us, asking and answering questions, and making predictions. Scientists use the scientific method because it is objective and based on evidence. A hypothesis is fundamental to the scientific method. A hypothesis can take the form of an explanation or a prediction. There are several ways to break down the steps of the scientific method, but it always involves forming a hypothesis, testing the hypothesis, and determining whether or not the hypothesis is correct.

Typical Steps of the Scientific Method

 Basically, the scientific method consists of these steps:

• Make observations.
• Propose  a hypothesis .
• Design and conduct and experiment  to test the hypothesis.
• Analyze the results of the experiment to form a conclusion.
• Determine whether or not the hypothesis is accepted or rejected.
• State the results.

If the hypothesis is rejected, this does  not  mean the experiment was a failure. In fact, if you proposed a null hypothesis (the easiest to test), rejecting the hypothesis may be sufficient to state the results. Sometimes, if the hypothesis is rejected, you reformulate the hypothesis or discard it and then go back to the experimentation stage.

Advantage of a Flow Chart

While it's easy to state the steps of the scientific method, a flow chart helps because it offers options at each point of the decision-making process. It tells you what to do next and makes it easier to visualize and plan an experiment.

Example of How to Use the Scientific Method Flow Chart

Following the flow chart:

The first step in following the scientific method is to make observations. Sometimes people omit this step from the scientific method, but everyone makes observations about a subject, even if it's informally. Ideally, you want to take notes of observations because this information may be used to help formulate a hypothesis.

Following the flow chart arrow, the next step is to construct a hypothesis. This is a prediction of what you think will happen if you change one thing. This "thing" that you change is called the independent variable . You measure what you think will change: the dependent variable . The hypothesis may be stated as an "if-then" statement. For example, "If the classroom lighting is changed to red, then student will do worse on tests." The color of the lighting (the variable you control) is the independent variable. The effect on student test grade is dependent on the lighting and is the dependent variable.

The next step is to design an experiment to test the hypothesis. Experimental design is important because a poorly designed experiment can lead a researcher to draw the wrong conclusions. To test whether red light worsens student test scores, you want to compare test scores from exams taken under normal lighting to those taken under red lighting. Ideally, the experiment would involve a large group of students, both taking the same test (such as two sections of a large class). Collect data from the experiment (the test scores) and determine whether the scores are higher, lower, or the same compared with the test under normal lighting (the results).

Following the flow chart, next you draw a conclusion. For example, if test scores were worse under the red light, then you accept the hypothesis and report the results. However, if the test scores under the red light were the same or higher than those taken under normal lighting, then you reject the hypothesis. From here, you follow the flow chart to construct a new hypothesis, which will be tested with an experiment.

If you learn the scientific method with a different number of steps, you can easily produce your own flow chart to describe the steps in the decision-making process!

• American Society of Mechanical Engineers (1947).  ASME Standard; Operation and Flow Process Charts . New York​.
• Franklin, James (2009).  What Science Knows: And How It Knows It . New York: Encounter Books. ISBN 978-1-59403-207-3.
• Gilbreth, Frank Bunker; Gilbreth, Lillian Moller (1921). ​ Process Charts . American Society of Mechanical Engineers.
• Losee, John (1980).  A Historical Introduction to the Philosophy of Science  (2nd edition). Oxford University Press, Oxford.
• Salmon, Wesley C. (1990).  Four Decades of Scientific Explanation . University of Minnesota Press, Minneapolis, MN.
• Examples of Independent and Dependent Variables
• Null Hypothesis Examples
• Difference Between Independent and Dependent Variables
• The Difference Between Control Group and Experimental Group
• Six Steps of the Scientific Method
• What Is an Experiment? Definition and Design
• How To Design a Science Fair Experiment
• What Is a Hypothesis? (Science)
• Scientific Method Lesson Plan
• What Are the Elements of a Good Hypothesis?
• Independent Variable Definition and Examples
• Scientific Method Vocabulary Terms
• Understanding Simple vs Controlled Experiments
• Science Projects for Every Subject
• Dependent Variable Definition and Examples

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• Guide to Experimental Design | Overview, Steps, & Examples

Guide to Experimental Design | Overview, 5 steps & Examples

Published on December 3, 2019 by Rebecca Bevans . Revised on June 21, 2023.

Experiments are used to study causal relationships . You manipulate one or more independent variables and measure their effect on one or more dependent variables.

Experimental design create a set of procedures to systematically test a hypothesis . A good experimental design requires a strong understanding of the system you are studying.

There are five key steps in designing an experiment:

• Consider your variables and how they are related
• Write a specific, testable hypothesis
• Design experimental treatments to manipulate your independent variable
• Assign subjects to groups, either between-subjects or within-subjects
• Plan how you will measure your dependent variable

For valid conclusions, you also need to select a representative sample and control any  extraneous variables that might influence your results. If random assignment of participants to control and treatment groups is impossible, unethical, or highly difficult, consider an observational study instead. This minimizes several types of research bias, particularly sampling bias , survivorship bias , and attrition bias as time passes.

Table of contents

Step 1: define your variables, step 2: write your hypothesis, step 3: design your experimental treatments, step 4: assign your subjects to treatment groups, step 5: measure your dependent variable, other interesting articles, frequently asked questions about experiments.

You should begin with a specific research question . We will work with two research question examples, one from health sciences and one from ecology:

To translate your research question into an experimental hypothesis, you need to define the main variables and make predictions about how they are related.

Start by simply listing the independent and dependent variables .

Then you need to think about possible extraneous and confounding variables and consider how you might control  them in your experiment.

Finally, you can put these variables together into a diagram. Use arrows to show the possible relationships between variables and include signs to show the expected direction of the relationships.

Here we predict that increasing temperature will increase soil respiration and decrease soil moisture, while decreasing soil moisture will lead to decreased soil respiration.

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Now that you have a strong conceptual understanding of the system you are studying, you should be able to write a specific, testable hypothesis that addresses your research question.

The next steps will describe how to design a controlled experiment . In a controlled experiment, you must be able to:

• Systematically and precisely manipulate the independent variable(s).
• Precisely measure the dependent variable(s).
• Control any potential confounding variables.

If your study system doesn’t match these criteria, there are other types of research you can use to answer your research question.

How you manipulate the independent variable can affect the experiment’s external validity – that is, the extent to which the results can be generalized and applied to the broader world.

First, you may need to decide how widely to vary your independent variable.

• just slightly above the natural range for your study region.
• over a wider range of temperatures to mimic future warming.
• over an extreme range that is beyond any possible natural variation.

Second, you may need to choose how finely to vary your independent variable. Sometimes this choice is made for you by your experimental system, but often you will need to decide, and this will affect how much you can infer from your results.

• a categorical variable : either as binary (yes/no) or as levels of a factor (no phone use, low phone use, high phone use).
• a continuous variable (minutes of phone use measured every night).

How you apply your experimental treatments to your test subjects is crucial for obtaining valid and reliable results.

First, you need to consider the study size : how many individuals will be included in the experiment? In general, the more subjects you include, the greater your experiment’s statistical power , which determines how much confidence you can have in your results.

Then you need to randomly assign your subjects to treatment groups . Each group receives a different level of the treatment (e.g. no phone use, low phone use, high phone use).

You should also include a control group , which receives no treatment. The control group tells us what would have happened to your test subjects without any experimental intervention.

When assigning your subjects to groups, there are two main choices you need to make:

• A completely randomized design vs a randomized block design .
• A between-subjects design vs a within-subjects design .

Randomization

An experiment can be completely randomized or randomized within blocks (aka strata):

• In a completely randomized design , every subject is assigned to a treatment group at random.
• In a randomized block design (aka stratified random design), subjects are first grouped according to a characteristic they share, and then randomly assigned to treatments within those groups.

Sometimes randomization isn’t practical or ethical , so researchers create partially-random or even non-random designs. An experimental design where treatments aren’t randomly assigned is called a quasi-experimental design .

Between-subjects vs. within-subjects

In a between-subjects design (also known as an independent measures design or classic ANOVA design), individuals receive only one of the possible levels of an experimental treatment.

In medical or social research, you might also use matched pairs within your between-subjects design to make sure that each treatment group contains the same variety of test subjects in the same proportions.

In a within-subjects design (also known as a repeated measures design), every individual receives each of the experimental treatments consecutively, and their responses to each treatment are measured.

Within-subjects or repeated measures can also refer to an experimental design where an effect emerges over time, and individual responses are measured over time in order to measure this effect as it emerges.

Counterbalancing (randomizing or reversing the order of treatments among subjects) is often used in within-subjects designs to ensure that the order of treatment application doesn’t influence the results of the experiment.

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Finally, you need to decide how you’ll collect data on your dependent variable outcomes. You should aim for reliable and valid measurements that minimize research bias or error.

Some variables, like temperature, can be objectively measured with scientific instruments. Others may need to be operationalized to turn them into measurable observations.

• Ask participants to record what time they go to sleep and get up each day.
• Ask participants to wear a sleep tracker.

How precisely you measure your dependent variable also affects the kinds of statistical analysis you can use on your data.

Experiments are always context-dependent, and a good experimental design will take into account all of the unique considerations of your study system to produce information that is both valid and relevant to your research question.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Likert scale

Research bias

• Implicit bias
• Framing effect
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic

Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:

• A testable hypothesis
• At least one independent variable that can be precisely manipulated
• At least one dependent variable that can be precisely measured

When designing the experiment, you decide:

• How you will manipulate the variable(s)
• How you will control for any potential confounding variables
• How many subjects or samples will be included in the study
• How subjects will be assigned to treatment levels

Experimental design is essential to the internal and external validity of your experiment.

The key difference between observational studies and experimental designs is that a well-done observational study does not influence the responses of participants, while experiments do have some sort of treatment condition applied to at least some participants by random assignment .

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word “between” means that you’re comparing different conditions between groups, while the word “within” means you’re comparing different conditions within the same group.

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

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How to Make an Experimental Flow Chart

By George Lawrence J.D.

Flow charts help explain a process by organizing the information visually and breaking up each step or element of the process into separate symbols on the chart. You can use a flow chart to document a scientific experiment. Each part of the experimental flow chart should document the critical steps of the experiment including your initial observations, hypothesis, what you did to test your hypothesis, the results, explanations of the results and your conclusion.

Develop a set of symbols for your flow chart. For example, the NASA Science Files suggests using ovals for start and stop points, hexagons for observations, circles for answers, diamonds for questions or decision points and rectangles for steps.

Draw your "Start" symbol and describe the experiment. For example, if the experiment is plant growth, you can write, "Start: Effects of Light on Plant Growth."

Draw another arrow from the observations to a box listing your explanations and/or a box stating your hypothesis about the observations.

Draw an arrow from the start box to a second box listing your observations. Branch arrows from your observation box and write in your initial observations about the experiment.

Connect arrows from your hypothesis box to the steps you need to take to prove or disprove your hypothesis. Continue connecting boxes with arrows in your flow chart to cover your findings, explanations of your results, and a final conclusion.

• EDrawsoft.com: Flowcharts

Based in Traverse City, Mich., George Lawrence has been writing professionally since 2009. His work primarily appears on various websites. An avid outdoorsman, Lawrence holds Bachelor of Arts degrees in both criminal justice and English from Michigan State University, as well as a Juris Doctor from the Thomas M. Cooley Law School, where he graduated with honors.

The Scientific Method Tutorial

The Scientific Method

Steps in the scientific method.

There is a great deal of variation in the specific techniques scientists use explore the natural world. However, the following steps characterize the majority of scientific investigations:

Step 1: Make observations Step 2: Propose a hypothesis to explain observations Step 3: Test the hypothesis with further observations or experiments Step 4: Analyze data Step 5: State conclusions about hypothesis based on data analysis

Each of these steps is explained briefly below, and in more detail later in this section.

Step 1: Make observations

A scientific inquiry typically starts with observations. Often, simple observations will trigger a question in the researcher's mind.

Example: A biologist frequently sees monarch caterpillars feeding on milkweed plants, but rarely sees them feeding on other types of plants. She wonders if it is because the caterpillars prefer milkweed over other food choices.

Step 2: Propose a hypothesis

The researcher develops a hypothesis (singular) or hypotheses (plural) to explain these observations. A hypothesis is a tentative explanation of a phenomenon or observation(s) that can be supported or falsified by further observations or experimentation.

Example: The researcher hypothesizes that monarch caterpillars prefer to feed on milkweed compared to other common plants. (Notice how the hypothesis is a statement, not a question as in step 1.)

Step 3: Test the hypothesis

The researcher makes further observations and/or may design an experiment to test the hypothesis. An experiment is a controlled situation created by a researcher to test the validity of a hypothesis. Whether further observations or an experiment is used to test the hypothesis will depend on the nature of the question and the practicality of manipulating the factors involved.

Example: The researcher sets up an experiment in the lab in which a number of monarch caterpillars are given a choice between milkweed and a number of other common plants to feed on.

Step 4: Analyze data

The researcher summarizes and analyzes the information, or data, generated by these further observations or experiments.

Example: In her experiment, milkweed was chosen by caterpillars 9 times out of 10 over all other plant selections.

Step 5: State conclusions

The researcher interprets the results of experiments or observations and forms conclusions about the meaning of these results. These conclusions are generally expressed as probability statements about their hypothesis.

Example: She concludes that when given a choice, 90 percent of monarch caterpillars prefer to feed on milkweed over other common plants.

Often, the results of one scientific study will raise questions that may be addressed in subsequent research. For example, the above study might lead the researcher to wonder why monarchs seem to prefer to feed on milkweed, and she may plan additional experiments to explore this question. For example, perhaps the milkweed has higher nutritional value than other available plants.

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The Scientific Method Flowchart

The steps in the scientific method are presented visually in the following flow chart. The question raised or the results obtained at each step directly determine how the next step will proceed. Following the flow of the arrows, pass the cursor over each blue box. An explanation and example of each step will appear. As you read the example given at each step, see if you can predict what the next step will be.

Activity: Apply the Scientific Method to Everyday Life Use the steps of the scientific method described above to solve a problem in real life. Suppose you come home one evening and flick the light switch only to find that the light doesn’t turn on. What is your hypothesis? How will you test that hypothesis? Based on the result of this test, what are your conclusions? Follow your instructor's directions for submitting your response.

The above flowchart illustrates the logical sequence of conclusions and decisions in a typical scientific study. There are some important points to note about this process:

1. The steps are clearly linked.

The steps in this process are clearly linked. The hypothesis, formed as a potential explanation for the initial observations, becomes the focus of the study. The hypothesis will determine what further observations are needed or what type of experiment should be done to test its validity. The conclusions of the experiment or further observations will either be in agreement with or will contradict the hypothesis. If the results are in agreement with the hypothesis, this does not prove that the hypothesis is true! In scientific terms, it "lends support" to the hypothesis, which will be tested again and again under a variety of circumstances before researchers accept it as a fairly reliable description of reality.

2. The same steps are not followed in all types of research.

The steps described above present a generalized method followed in a many scientific investigations. These steps are not carved in stone. The question the researcher wishes to answer will influence the steps in the method and how they will be carried out. For example, astronomers do not perform many experiments as defined here. They tend to rely on observations to test theories. Biologists and chemists have the ability to change conditions in a test tube and then observe whether the outcome supports or invalidates their starting hypothesis, while astronomers are not able to change the path of Jupiter around the Sun and observe the outcome!

3. Collected observations may lead to the development of theories.

When a large number of observations and/or experimental results have been compiled, and all are consistent with a generalized description of how some element of nature operates, this description is called a theory. Theories are much broader than hypotheses and are supported by a wide range of evidence. Theories are important scientific tools. They provide a context for interpretation of new observations and also suggest experiments to test their own validity. Theories are discussed in more detail in another section.

The Scientific Method in Detail

In the sections that follow, each step in the scientific method is described in more detail.

Step 1: Observations

Observations in science.

An observation is some thing, event, or phenomenon that is noticed or observed. Observations are listed as the first step in the scientific method because they often provide a starting point, a source of questions a researcher may ask. For example, the observation that leaves change color in the fall may lead a researcher to ask why this is so, and to propose a hypothesis to explain this phenomena. In fact, observations also will provide the key to answering the research question.

In science, observations form the foundation of all hypotheses, experiments, and theories. In an experiment, the researcher carefully plans what observations will be made and how they will be recorded. To be accepted, scientific conclusions and theories must be supported by all available observations. If new observations are made which seem to contradict an established theory, that theory will be re-examined and may be revised to explain the new facts. Observations are the nuts and bolts of science that researchers use to piece together a better understanding of nature.

Observations in science are made in a way that can be precisely communicated to (and verified by) other researchers. In many types of studies (especially in chemistry, physics, and biology), quantitative observations are used. A quantitative observation is one that is expressed and recorded as a quantity, using some standard system of measurement. Quantities such as size, volume, weight, time, distance, or a host of others may be measured in scientific studies.

Some observations that researchers need to make may be difficult or impossible to quantify. Take the example of color. Not all individuals perceive color in exactly the same way. Even apart from limiting conditions such as colorblindness, the way two people see and describe the color of a particular flower, for example, will not be the same. Color, as perceived by the human eye, is an example of a qualitative observation.

Qualitative observations note qualities associated with subjects or samples that are not readily measured. Other examples of qualitative observations might be descriptions of mating behaviors, human facial expressions, or "yes/no" type of data, where some factor is present or absent. Though the qualities of an object may be more difficult to describe or measure than any quantities associated with it, every attempt is made to minimize the effects of the subjective perceptions of the researcher in the process. Some types of studies, such as those in the social and behavioral sciences (which deal with highly variable human subjects), may rely heavily on qualitative observations.

Question: Why are observations important to science?

Limits of Observations

Because all observations rely to some degree on the senses (eyes, ears, or steady hand) of the researcher, complete objectivity is impossible. Our human perceptions are limited by the physical abilities of our sense organs and are interpreted according to our understanding of how the world works, which can be influenced by culture, experience, or education. According to science education specialist, George F. Kneller, "Surprising as it may seem, there is no fact that is not colored by our preconceptions" ("A Method of Enquiry," from Science and Its Ways of Knowing [Upper Saddle River: Prentice-Hall Inc., 1997], 15).

Observations made by a scientist are also limited by the sensitivity of whatever equipment he is using. Research findings will be limited at times by the available technology. For example, Italian physicist and philosopher Galileo Galilei (1564–1642) was reportedly the first person to observe the heavens with a telescope. Imagine how it must have felt to him to see the heavens through this amazing new instrument! It opened a window to the stars and planets and allowed new observations undreamed of before.

In the centuries since Galileo, increasingly more powerful telescopes have been devised that dwarf the power of that first device. In the past decade, we have marveled at images from deep space , courtesy of the Hubble Space Telescope, a large telescope that orbits Earth. Because of its view from outside the distorting effects of the atmosphere, the Hubble can look 50 times farther into space than the best earth-bound telescopes, and resolve details a tenth of the size (Seeds, Michael A., Horizons: Exploring the Universe , 5 th ed. [Belmont: Wadsworth Publishing Company, 1998], 86-87).

Construction is underway on a new radio telescope that scientists say will be able to detect electromagnetic waves from the very edges of the universe! This joint U.S.-Mexican project may allow us to ask questions about the origins of the universe and the beginnings of time that we could never have hoped to answer before. Completion of the new telescope is expected by the end of 2001.

Although the amount of detail observed by Galileo and today's astronomers is vastly different, the stars and their relationships have not changed very much. Yet with each technological advance, the level of detail of observation has been increased, and with it, the power to answer more and more challenging questions with greater precision.

Question: What are some of the differences between a casual observation and a 'scientific observation'?

Step 2: The Hypothesis

A hypothesis is a statement created by the researcher as a potential explanation for an observation or phenomena. The hypothesis converts the researcher's original question into a statement that can be used to make predictions about what should be observed if the hypothesis is true. For example, given the hypothesis, "exposure to ultraviolet (UV) radiation increases the risk of skin cancer," one would predict higher rates of skin cancer among people with greater UV exposure. These predictions could be tested by comparing skin cancer rates among individuals with varying amounts of UV exposure. Note how the hypothesis itself determines what experiments or further observations should be made to test its validity. Results of tests are then compared to predictions from the hypothesis, and conclusions are stated in terms of whether or not the data supports the hypothesis. So the hypothesis serves a guide to the full process of scientific inquiry.

The Qualities of a Good Hypothesis

• A hypothesis must be testable or provide predictions that are testable. It can potentially be shown to be false by further observations or experimentation.
• A hypothesis should be specific. If it is too general it cannot be tested, or tests will have so many variables that the results will be complicated and difficult to interpret. A well-written hypothesis is so specific it actually determines how the experiment should be set up.
• A hypothesis should not include any untested assumptions if they can be avoided. The hypothesis itself may be an assumption that is being tested, but it should be phrased in a way that does not include assumptions that are not tested in the experiment.
• It is okay (and sometimes a good idea) to develop more than one hypothesis to explain a set of observations. Competing hypotheses can often be tested side-by-side in the same experiment.

Question: Why is the hypothesis important to the scientific method?

Step 3: Testing the Hypothesis

A hypothesis may be tested in one of two ways: by making additional observations of a natural situation, or by setting up an experiment. In either case, the hypothesis is used to make predictions, and the observations or experimental data collected are examined to determine if they are consistent or inconsistent with those predictions. Hypothesis testing, especially through experimentation, is at the core of the scientific process. It is how scientists gain a better understanding of how things work.

Testing a Hypothesis by Observation

Some hypotheses may be tested through simple observation. For example, a researcher may formulate the hypothesis that the sun always rises in the east. What might an alternative hypothesis be? If his hypothesis is correct, he would predict that the sun will rise in the east tomorrow. He can easily test such a prediction by rising before dawn and going out to observe the sunrise. If the sun rises in the west, he will have disproved the hypothesis. He will have shown that it does not hold true in every situation. However, if he observes on that morning that the sun does in fact rise in the east, he has not proven the hypothesis. He has made a single observation that is consistent with, or supports, the hypothesis. As a scientist, to confidently state that the sun will always rise in the east, he will want to make many observations, under a variety of circumstances. Note that in this instance no manipulation of circumstance is required to test the hypothesis (i.e., you aren't altering the sun in any way).

Testing a Hypothesis by Experimentation

An experiment is a controlled series of observations designed to test a specific hypothesis. In an experiment, the researcher manipulates factors related to the hypothesis in such a way that the effect of these factors on the observations (data) can be readily measured and compared. Most experiments are an attempt to define a cause-and-effect relationship between two factors or events—to explain why something happens. For example, with the hypothesis "roses planted in sunny areas bloom earlier than those grown in shady areas," the experiment would be testing a cause-and-effect relationship between sunlight and time of blooming.

A major advantage of setting up an experiment versus making observations of what is already available is that it allows the researcher to control all the factors or events related to the hypothesis, so that the true cause of an event can be more easily isolated. In all cases, the hypothesis itself will determine the way the experiment will be set up. For example, suppose my hypothesis is "the weight of an object is proportional to the amount of time it takes to fall a certain distance." How would you test this hypothesis?

The Qualities of a Good Experiment

• The experiment must be conducted on a group of subjects that are narrowly defined and have certain aspects in common. This is the group to which any conclusions must later be confined. (Examples of possible subjects: female cancer patients over age 40, E. coli bacteria, red giant stars, the nicotine molecule and its derivatives.)
• All subjects of the experiment should be (ideally) completely alike in all ways except for the factor or factors that are being tested. Factors that are compared in scientific experiments are called variables. A variable is some aspect of a subject or event that may differ over time or from one group of subjects to another. For example, if a biologist wanted to test the effect of nitrogen on grass growth, he would apply different amounts of nitrogen fertilizer to several plots of grass. The grass in each of the plots should be as alike as possible so that any difference in growth could be attributed to the effect of the nitrogen. For example, all the grass should be of the same species, planted at the same time and at the same density, receive the same amount of water and sunlight, and so on. The variable in this case would be the amount of nitrogen applied to the plants. The researcher would not compare differing amounts of nitrogen across different grass species to determine the effect of nitrogen on grass growth. What is the problem with using different species of plants to compare the effect of nitrogen on plant growth? There are different kinds of variables in an experiment. A factor that the experimenter controls, and changes intentionally to determine if it has an effect, is called an independent variable . A factor that is recorded as data in the experiment, and which is compared across different groups of subjects, is called a dependent variable . In many cases, the value of the dependent variable will be influenced by the value of an independent variable. The goal of the experiment is to determine a cause-and-effect relationship between independent and dependent variables—in this case, an effect of nitrogen on plant growth. In the nitrogen/grass experiment, (1) which factor was the independent variable? (2) Which factor was the dependent variable?
• Nearly all types of experiments require a control group and an experimental group. The control group generally is not changed in any way, but remains in a "natural state," while the experimental group is modified in some way to examine the effect of the variable which of interest to the researcher. The control group provides a standard of comparison for the experimental groups. For example, in new drug trials, some patients are given a placebo while others are given doses of the drug being tested. The placebo serves as a control by showing the effect of no drug treatment on the patients. In research terminology, the experimental groups are often referred to as treatments , since each group is treated differently. In the experimental test of the effect of nitrogen on grass growth, what is the control group? In the example of the nitrogen experiment, what is the purpose of a control group?
• In research studies a great deal of emphasis is placed on repetition. It is essential that an experiment or study include enough subjects or enough observations for the researcher to make valid conclusions. The two main reasons why repetition is important in scientific studies are (1) variation among subjects or samples and (2) measurement error.

Variation among Subjects

There is a great deal of variation in nature. In a group of experimental subjects, much of this variation may have little to do with the variables being studied, but could still affect the outcome of the experiment in unpredicted ways. For example, in an experiment designed to test the effects of alcohol dose levels on reflex time in 18- to 22-year-old males, there would be significant variation among individual responses to various doses of alcohol. Some of this variation might be due to differences in genetic make-up, to varying levels of previous alcohol use, or any number of factors unknown to the researcher.

Because what the researcher wants to discover is average dose level effects for this group, he must run the test on a number of different subjects. Suppose he performed the test on only 10 individuals. Do you think the average response calculated would be the same as the average response of all 18- to 22-year-old males? What if he tests 100 individuals, or 1,000? Do you think the average he comes up with would be the same in each case? Chances are it would not be. So which average would you predict would be most representative of all 18- to 22-year-old males?

A basic rule of statistics is, the more observations you make, the closer the average of those observations will be to the average for the whole population you are interested in. This is because factors that vary among a population tend to occur most commonly in the middle range, and least commonly at the two extremes. Take human height for example. Although you may find a man who is 7 feet tall, or one who is 4 feet tall, most men will fall somewhere between 5 and 6 feet in height. The more men we measure to determine average male height, the less effect those uncommon extreme (tall or short) individuals will tend to impact the average. Thus, one reason why repetition is so important in experiments is that it helps to assure that the conclusions made will be valid not only for the individuals tested, but also for the greater population those individuals represent.

"The use of a sample (or subset) of a population, an event, or some other aspect of nature for an experimental group that is not large enough to be representative of the whole" is called sampling error (Starr, Cecie, Biology: Concepts and Applications , 4 th ed. [Pacific Cove: Brooks/Cole, 2000], glossary). If too few samples or subjects are used in an experiment, the researcher may draw incorrect conclusions about the population those samples or subjects represent.

Use the jellybean activity below to see a simple demonstration of samping error.

Directions: There are 400 jellybeans in the jar. If you could not see the jar and you initially chose 1 green jellybean from the jar, you might assume the jar only contains green jelly beans. The jar actually contains both green and black jellybeans. Use the "pick 1, 5, or 10" buttons to create your samples. For example, use the "pick" buttons now to create samples of 2, 13, and 27 jellybeans. After you take each sample, try to predict the ratio of green to black jellybeans in the jar. How does your prediction of the ratio of green to black jellybeans change as your sample changes?

Measurement Error

The second reason why repetition is necessary in research studies has to do with measurement error. Measurement error may be the fault of the researcher, a slight difference in measuring techniques among one or more technicians, or the result of limitations or glitches in measuring equipment. Even the most careful researcher or the best state-of-the-art equipment will make some mistakes in measuring or recording data. Another way of looking at this is to say that, in any study, some measurements will be more accurate than others will. If the researcher is conscientious and the equipment is good, the majority of measurements will be highly accurate, some will be somewhat inaccurate, and a few may be considerably inaccurate. In this case, the same reasoning used above also applies here: the more measurements taken, the less effect a few inaccurate measurements will have on the overall average.

Step 4: Data Analysis

In any experiment, observations are made, and often, measurements are taken. Measurements and observations recorded in an experiment are referred to as data . The data collected must relate to the hypothesis being tested. Any differences between experimental and control groups must be expressed in some way (often quantitatively) so that the groups may be compared. Graphs and charts are often used to visualize the data and to identify patterns and relationships among the variables.

Statistics is the branch of mathematics that deals with interpretation of data. Data analysis refers to statistical methods of determining whether any differences between the control group and experimental groups are too great to be attributed to chance alone. Although a discussion of statistical methods is beyond the scope of this tutorial, the data analysis step is crucial because it provides a somewhat standardized means for interpreting data. The statistical methods of data analysis used, and the results of those analyses, are always included in the publication of scientific research. This convention limits the subjective aspects of data interpretation and allows scientists to scrutinize the working methods of their peers.

Why is data analysis an important step in the scientific method?

Step 5: Stating Conclusions

The conclusions made in a scientific experiment are particularly important. Often, the conclusion is the only part of a study that gets communicated to the general public. As such, it must be a statement of reality, based upon the results of the experiment. To assure that this is the case, the conclusions made in an experiment must (1) relate back to the hypothesis being tested, (2) be limited to the population under study, and (3) be stated as probabilities.

The hypothesis that is being tested will be compared to the data collected in the experiment. If the experimental results contradict the hypothesis, it is rejected and further testing of that hypothesis under those conditions is not necessary. However, if the hypothesis is not shown to be wrong, that does not conclusively prove that it is right! In scientific terms, the hypothesis is said to be "supported by the data." Further testing will be done to see if the hypothesis is supported under a number of trials and under different conditions.

If the hypothesis holds up to extensive testing then the temptation is to claim that it is correct. However, keep in mind that the number of experiments and observations made will only represent a subset of all the situations in which the hypothesis may potentially be tested. In other words, experimental data will only show part of the picture. There is always the possibility that a further experiment may show the hypothesis to be wrong in some situations. Also, note that the limits of current knowledge and available technologies may prevent a researcher from devising an experiment that would disprove a particular hypothesis.

The researcher must be sure to limit his or her conclusions to apply only to the subjects tested in the study. If a particular species of fish is shown to consume their young 90 percent of the time when raised in captivity, that doesn't necessarily mean that all fish will do so, or that this fish's behavior would be the same in its native habitat.

Finally, the conclusions of the experiment are generally stated as probabilities. A careful scientist would never say, "drug x kills cancer cells;" she would more likely say, "drug x was shown to destroy 85 percent of cancerous skin cells in rats in lab trials." Notice how very different these two statements are. There is a tendency in the media and in the general public to gravitate toward the first statement. This makes a terrific headline and is also easy to interpret; it is absolute. Remember though, in science conclusions must be confined to the population under study; broad generalizations should be avoided. The second statement is sound science. There is data to back it up. Later studies may reveal a more universal effect of the drug on cancerous cells, or they may not. Most researchers would be unwilling to stake their reputations on the first statement.

As a student, you should read and interpret popular press articles about research studies very carefully. From the text, can you determine how the experiment was set up and what variables were measured? Are the observations and data collected appropriate to the hypothesis being tested? Are the conclusions supported by the data? Are the conclusions worded in a scientific context (as probability statements) or are they generalized for dramatic effect? In any researched-based assignment, it is a good idea to refer to the original publication of a study (usually found in professional journals) and to interpret the facts for yourself.

Qualities of a Good Experiment

• narrowly defined subjects
• all subjects treated alike except for the factor or variable being studied
• a control group is used for comparison
• measurements related to the factors being studied are carefully recorded
• enough samples or subjects are used so that conclusions are valid for the population of interest
• conclusions made relate back to the hypothesis, are limited to the population being studied, and are stated in terms of probabilities

Scientific Method Steps and Flow Chart

Observation : This is the initial step where you notice something interesting or unusual in the natural world.

Question : After making an observation, you form a question about what you observed. This question should be specific and testable.

Hypothesis : Based on your observation and question, you propose a tentative explanation called a hypothesis. This hypothesis should be a statement that can be tested through experimentation.

Prediction : Once you have a hypothesis, you make predictions about what you expect to happen if the hypothesis is true. These predictions should be based on logical reasoning and can be used to design experiments.

Experimentation : This step involves designing and conducting experiments to test your hypothesis and predictions. Experiments should be carefully controlled to ensure that only one variable is changed at a time (the independent variable), while all other variables are kept constant.

Data Collection : During the experiment, you collect data by making observations and measurements. This data should be recorded systematically and accurately.

Analysis : After collecting data, you analyze it to determine whether it supports or refutes your hypothesis. This may involve statistical analysis or other methods of data interpretation.

Conclusion : Based on the analysis of your data, you draw conclusions about whether your hypothesis was supported or not. If your hypothesis was supported, you may propose additional experiments to further validate your findings. If your hypothesis was not supported, you may revise it and repeat the process.

Communication : Finally, you communicate your findings to others through scientific journals, presentations, or other means. This allows other scientists to review and replicate your experiments, further contributing to the scientific knowledge base.

Related Resources

Investigation:  What Are the Processes of Science  – students design an experiment about lung capacity; requires spirometers, AP Biology

Sponge Capsules  – quick lab using capsules and water (toys) to collect data on how fast the “animals” grow

Investigation – Heat Storage and Loss  – Use a jar and different types of insulation to explore how heat is lost and which materials are better insulators ( Key, TpT )

Investigation: What Factors Affect Seed Germination  – simple experiment where students use beans and different variables (water, light, temperature)

Written by Wun Chiou

(A Former UCLA First-Year Lab Courses Teaching Assistant)

A lab report is more than just something you turn in to (hopefully) get a good grade. It's your opportunity to show that you understand what is going on in the experiment, which is really the most important part of doing it. In addition, I think it's actually very good practice for getting across your thoughts about the science you are doing in a manner that the reader can understand.

What you write in your laboratory notebook is an actual account of what you have done in a given experiment, like a very detailed diary. You should be able to come back to it at some point, read what you wrote before, and reproduce what you did before. So should anyone else reading your notebook, for that matter. That way, if you make some amazing discovery, like blue aspirin is better than white aspirin (btw: don't eat anything in, from, or created in lab to see if this is right), you will have a permanent record of it to remind you of your greatness. There are three basic parts to a lab report: pre-lab , in-lab , and post-lab . In this document, I've written some helpful tips that might help you through your lab-report woes. I won't include everything you have to do (you should look on VOH for the report guidelines), but just a few key ideas.

PRE-LAB REPORT

I. Introduction

The introduction discusses the problem being studied and the relevant theory. Ideally, it would take up about 4-5 sentences. The main idea here is to give the reader an idea of what you are going to do in a short paragraph. There are different styles to do this. You should try to write it in your own words, rather than paraphrasing or quoting the lab manual (but if you have to, be sure to include the appropriate references). It's always a good idea to read the entire experiment in the manual before you begin your introduction. I suggest the following:

Background sentences: state why you want to do the experiment, why is it relevant, what other kinds of similar experiments have been done in the past.

Goal: In one sentence, state what you are going to do in the experiment and what you hope to find. This is probably the most important part of the introduction. You should also list explicitly any main chemicals with which you are dealing (vinegar, aspirin, NaOH) and any techniques you will be utilizing (titration, recrystallization, spectrophotometry, etc.). For example, "In this experiment, we will determine the buffer capacity of a weak acid buffer of acetic acid / acetate ion by titration with both a strong acid, HCl, and a strong base, NaOH."

Other procedures or theory: If you need to elaborate on some of the techniques you stated in your goal (or couldn't state in your goal), you can write a couple more sentences about them afterwards. Or you can add anything else that you might think is relevant, like additional major procedural steps you will take.

Keep it sho rt!

II. Procedural Flowchart

This part of the pre-lab should take no more than one page. A good flowchart should give a reader an immediate idea of what's need to be done in the laboratory except in a less detailed format. Think of a flowchart as a "road map" of the experiment. It gives a reader a "pictorial" representation of the experimental procedure. In general there are two major steps when constructing the flowchart. First, read the experimental procedure carefully. Second, rewrite the procedures in a flowchart format. Keep in mind that the flowchart should be brief and cover all the steps in a simple and easy to follow manner. There should be no complicated sentences or paragraphs in the flowchart. You will have to do a lot of rewriting in order to simplify the procedures into a flowchart format. This is exactly why we want you to do it. This gives you a chance to THINK about what you read and how to rewrite it in a way that can be implemented into a flowchart.

Always remember to reference where the experimental procedures are coming from in the pre-lab report.

Please DO NOT simply copy the entire procedure (or majority of the procedure) and make it looks like a flowchart.

IN-LAB RESPONSIBILITY

I. Data-taking

Always write in pen. You can't really erase anything, anyway, because of the carbon paper below it. White-out is a big no-no, too. Always record data directly into your lab notebook. I know some people like to be neat, and have nice formatting and all that, but it's more important to make sure you record all of the data immediately in case you forget what you wanted to say later or you forget to copy other data into your notebook. Never scratch something out completely. Yeah, nobody's perfect and of course also nobody wants to be reminded of that, but you may discover that you were right in the first place, and now you wish you could read what you wrote before. Also, if you make a mistake it's a good idea to keep a record if it so you (or someone else trying to do your experiment) can remember to not make the same mistake twice.

II. Observations

In addition to writing down all those numbers (data), you should keep an eye (nose, ear, etc.) on what is actually happening in the experiment. If you add one thing to another and it evolves a gas, gets hot or cold, changes color or odor, precipitates a solid, reacts really quickly or slowly, or anything noticeable, you should write down that observation in your lab notebook. Other things to consider including are: make and type of any machine you are using, concentrations of all the standards you used, and etc. One of the reasons you are doing this goes back to what I said about mistakes earlier. An experiment is exactly that: an experiment. If it turns out that you get an unexpected result, you can go back and trace your observations to see where the error occurred. If you don't have any observat ions, this is really hard to do. The bottom line: write what you do and do what you write.

POST-LAB REPORT

I. Data again?

Recopy your data from the in-lab here in a nice neat format (tables are usually nice and neat). This is your chance to organize it into a more readable form now that you are done with the experiment and impress the TA with your organizational skills.

II. Calculations

It's a good idea to write out all the formulas you use in your calculations. Personally, I like to work through the problem using just the formula, and then plug in the numbers at the end to get my final answer. Also, show all of your work. One more point is to be sure to include the units when you are doing a calculation, and don't drop the units halfway through the calculation. This is actually a pretty powerful tool because if your answer has the wrong units you know you must have made an error somewhere along the way. Conversely, if your answer has the correct units, you could still be wrong, but at least you are on the right track (and probably much of the time your answer is correct, too!) You can even do the calculation using just units and no numbers and see if the units cancel out in the right way to test if you method is good (this is called dimensional analysis).

III. Conclusion

The conclusion is alot like the introduction except, instead of a summary of what you are going to do, it's a summary of what you did. The reason you have a conclusion is because your lab report might be long and the reader may not remember all the important points that you stated. Also, it gives you a chance to explain anything that might have gone wrong or could be improved, as well as propose future experiments. Like the introduction, it should be short and to the point. Again, these are only my suggestions, but here's what I think you should always include:

What you did: Reiterate your procedures briefly (including any changes you made).

What you found: Restate any results that you may have calculated (with errors if applicable). You don't need to include the raw data, but if you calculated an average over several trials, state the average (not each trial). Usually you want to report the results as x +/- y (like 2.345 +/- 0.003), where y is the absolute error in x. Another option, if you calculated the relative error, is x +/- z% (like 2.345 +/- 0.5%), where z is the relative error.

What you think: What do your results mean? Are they good? Bad? Why or why not? Basically, comment on the results. If your experimental error (RAD, RSD) is small or large compared to the inherent error (the error in the standards and equipment used), comment on what this means, too.

Errors: Speculate on possible sources of error.

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Let Your Ideas Flow: Using Flowcharts to Convey Methods and Implications of the Results in Laboratory Exercises, Articles, Posters, and Slide Presentations

Olivia kimber.

1 Department of Chemistry, West Chester University, West Chester, PA 19383

Jennifer G. Cromley

2 Department of Educational Psychology, College of Education, University of Illinois at Urbana-Champaign, Champaign, IL 61820

Katherine L. Molnar-Kimber

3 Kimnar Group LLC, KMK Consulting Services, Worcester, PA 19490

INTRODUCTION

Communicating science to peers and students often involves constructing clear, concise flow diagrams and illustrations as well as writing narratives ( 1 , 2 ). Diverse methods for learning, including the use of diagrams of complex biological pathways ( 2 ), help increase the number of active retrieval pathways, lengthen memory, and improve recall efficiency ( 3 ). Diagrams vary in their complexity, which should match the audience’s familiarity with the topic. High school, premedical, and medical students who reviewed a brief set of comic strips or a comic chapter book on the anatomy of the digestive system had greater recall of the organs’ functions than control students ( 4 ). A group of tenth-grade biology students who studied diagrams enhanced their restudy and recall of biology concepts ( 5 ). A different group of tenth-grade biology students improved their comprehension of scientific diagrams by participating in workbook-focused instruction on conventions of diagrams and discussions led by a teacher ( 6 ). Since even physicians have been shown to prefer flowcharts and flow diagrams for learning and recalling clinical guidelines ( 7 ), science communicators should consider flowcharts and diagrams as important tools for teaching or refreshing science concepts with students of most ages and levels. While we present tips and tools for teaching students in grades 6 through 12 and college, all scientists and communicators can use the same tips and tools (tables, figures, and URLs) to design effective diagrams that communicate complex processes in simple and engaging visual ways.

Both flowcharts and flow diagrams can help students and readers who learn through seeing comprehend the relationships between objects or steps. Some people can remember details from a picture with text for a longer time than details from prose: for example, pictures with text in patient leaflets improve recall, comprehension, and medication adherence by the general public ( 8 ). Flowcharts and flow diagrams commonly use brief text and graphic elements to give an overview of a multistep process, a theory, or comparisons ( Table 1 ). Although some students have better spatial cognition than others, all students who actively construct diagrams discover how to “follow the arrows” ( 9 , 10 ). Students who self-completed diagrams with both text and graphic elements but not diagrams with only graphic elements could transfer inferences to a different scientific field ( 11 ). Furthermore, teacher instruction with a workbook that explains the common meanings of symbols and graphic elements in the diagrams from their biology textbook improved students’ comprehension ( 6 ).

Common uses of flowcharts and flow diagrams.

The recently described Scientific Process Flowchart Assessment method can help evaluate students’ comprehension and visualization of the scientific process ( 12 ). Questions in prose help assess students’ grasp of concept definitions and facts but not how students organize the knowledge and relate it to similar fields ( 12 ). Diagrams can help students organize the knowledge ( 10 ) and apply it to other fields. Students who create flow diagrams may form a better-integrated or deeper understanding of the topic ( 10 ) because they need to notice all of the elements and their functions ( 13 ). In addition, flowcharts and flow diagrams are easy to understand for someone even with limited knowledge of the language; effective diagrams help students and scientists convey their research to peers and international colleagues at scientific meetings. Because students developing their own flowcharts may be able to more rapidly interpret flowcharts made by others ( 9 ), we encourage teachers to make construction or use of flowcharts a weekly activity, possibly as part of their preparation for laboratory exercises.

Teachers and professors can ask their students to prepare a flowchart of the procedure as pre-laboratory preparation rather than writing a description. Because people read English from left to right and top to bottom, steps in a flowchart also progress from top to bottom for more rapid comprehension and retention ( 14 ). Since many students do not know the conventions of diagrams, teachers can download and use an effective workbook developed by Cromley et al. ( 6 ) (workbook available at http://hdl.handle.net/2142/97891 ) for teaching them to tenth-grade biology students. Cromley et al. ( 6 , 11 ) provide an additional workbook focused on genetics, and two focused on atoms and proteins at the same URL. (When you use them, please provide feedback to Dr. Cromley.) Table 2 lists the basic conventions of diagrams—both text and graphic elements—with examples shown in Figures 1 and ​ and2. 2 . The life cycle ( Fig. 1 ) proceeds in a clockwise direction. Fig. 1A is typical of diagrams found in scientific articles: the listing of the hours after infection and the blue arrows show the passage of time and the sequence of events. In comparison, Figure 1B uses extra variation in colors of the conventions to emphasize the progression of the infectious process (arrows go from light to dark blue) and differentiate between the two types of infectious particles (EB = brown circle versus RB = beige circle). The changes in Figure 1B can help readers comprehend the life cycle more quickly. To be effective, multiple flowcharts and diagrams in a single manuscript or slide presentation should use the same conventions (e.g., same shape, size, color, filling) only for identical variables or processes ( 2 ). Students, communicators, and scientists can portray similar but distinct variables or processes with related graphic elements (e.g., gradated colored arrows, different colored elements for different stages of infectious particles in Fig. 1B ).

Explanation and examples of conventions of diagrams.

EB = elementary body; RB = reticulate body.

Flow diagrams showing the life cycle of Chlamydia , a pathogenic organism. A) Original diagram. B) Revised diagram uses similar conventions of diagrams and some modifications have been made to improve comprehension. EB = elementary body of Chlamydia ; RB = reticulate body of Chlamydia .

A flow diagram that zooms out (reduces magnification) from a DNA helix at high resolution through several more-compact structures to full condensation and into a metaphase chromosome.

Flow diagrams can show the relationship of a small part to a large part. For example, Figure 2 shows a model of chromosome condensation. It displays a naked DNA helix, and zooms out in five steps to show the structures in which the DNA lives, up to a condensed metaphase chromosome. This model does not provide the mechanisms and proteins involved in going from the DNA helix to the condensed metaphase chromosome. Thus, the designer needs to decide not only what to include, but also what to leave out to avoid too much information and clutter ( 14 ). To avoid audience overload in diagrams for slide presentations, diagram creators can group similar variables or processes together and show the relationships between four groups or fewer on a slide ( 14 ). Adding animation to highlight group X can help focus the audience’s attention on a specific point during the oral presentation. Since students who draw and use diagrams, especially those containing text ( 11 ), can more easily comprehend and apply the displayed scientific concepts in future biological and microbiological knowledge and research ( 10 , 15 ), scientists and communicators may wish to include at least naming and explanatory labels, graphic elements, and a detailed legend ( 2 ) in their flowcharts and diagrams.

Several vendors sell templates of biological shapes and some flow diagrams with a non-exclusive license for use of modified works. To save time and maintain quality, we modified two PowerPoint templates from the scientific illustration toolkits for presentations and publications from MOTIFOLIO to make flow diagrams for our figures. Scientists and communicators should consider using unambiguous conventions, especially unambiguous graphic elements, in their diagrams to optimize the audience’s focus at the start and support the appropriate flow of their attention to subsequent parts.

Flowcharts and diagrams help many people remember a sequence of events and recall interactions in a complex process. Teachers, professors, and science communicators can display information for their students and audiences via flowcharts in class discussions, on handouts, in articles, during continuing education programs, and in slide presentations. Explaining the conventions of diagrams in the figure legends and clearly indicating the sequence in which to read them can help all audiences better assimilate the relationships the flow diagram conveys. Students who use and make flowcharts as part of weekly laboratories may become more adept in applying complex concepts ( 15 ). Students of all ages, colleagues, and readers likely will appreciate these insights and enhanced communication skills.

ACKNOWLEDGMENTS

The authors declare that there are no conflicts of interest.