Lab Report Data Presentation

By Dr. Zubair Khalid, DVM, MS, PhD · 2026-08-27

Quick Answer

• Most lab report point loss comes from cluttered tables, mislabeled figures, and misleading data displays that obscure the actual experimental results.
• Fix each presentation error systematically using the checklist below, starting with axis labels and table headers before revising figure design.
• No single formatting rule applies to every journal or course, so always confirm the specific reporting guidelines required by your instructor or target publication.

At a Glance

Why Data Presentation Determines Your Lab Report Grade

Laboratory reports are judged on whether a reader can independently verify what you did and what you found. A well designed table or figure communicates your results in seconds. A poorly designed one forces the reader to guess at your meaning, and graders interpret that guesswork as weak science. The difference between a strong and weak report often has nothing to do with the quality of your experimental work. It comes down to whether your data displays follow basic visual communication principles.

Research reporting guidelines exist because transparent presentation matters across all scientific fields. The EQUATOR Network maintains a collection of reporting guidelines that help researchers present their methods and findings clearly and completely. These guidelines emphasize that readers should be able to understand what was done and what was found without ambiguity. When you present data in a lab report, you are engaging in the same practice of transparent reporting that professional journals require.

Your instructor likely applies similar standards when grading. A table with unclear headers or a figure with missing axis labels forces the grader to work harder than necessary. That extra effort translates into lost points, even when your underlying experimental results are sound.

The 10 Most Frequent Data Presentation Errors

Error 1: Unlabeled or Poorly Labeled Axes

The most common figure mistake is an axis without a clear title or without units. A reader cannot interpret a numerical value if they do not know what the number represents or in what units it was measured. Every axis in every figure must include a descriptive label and the appropriate unit of measurement.

For example, an axis labeled simply "Time" is incomplete. The reader needs to know whether time was measured in seconds, minutes, hours, or days. An axis labeled "Time (minutes)" is complete and unambiguous. The same principle applies to the dependent variable. "Absorbance" means little without a wavelength or unit. "Absorbance at 600 nm" provides the necessary context.

When you prepare a figure, check each axis against this standard. If you cannot determine what was measured and in what units from the axis label alone, the label needs revision.

Error 2: Table Headers Without Units

Tables present a different challenge than figures because the reader must scan rows and columns to extract meaning. When units appear only in the table title or in a footnote, the reader must constantly refer back to that information. The solution is to place units in the column headers.

A column header that reads "Weight" is incomplete. The same header reading "Weight (g)" tells the reader exactly what the numbers mean. This placement applies to every column that contains measured values. If different columns use different units, each column header must carry its own unit designation.

This rule also applies to row headers when the table is organized with variables in rows instead of columns. The unit belongs with the variable name, not scattered throughout the table body.

Error 3: Excessive Decimal Places

The number of decimal places in your data should reflect the precision of your measurement instrument. If your balance measures to the nearest 0.01 g, report values to two decimal places. Reporting values to four decimal places when your instrument only measures to two implies a precision you did not achieve.

This error appears most often when students copy raw data directly from spreadsheet software that retains every digit from a calculation. A calculated mean of 12.3456789 from measurements taken to two decimal places should be reported as 12.35. The extra digits do not add information. They create the false impression that your measurements were more precise than they actually were.

When you calculate statistics such as standard deviation or standard error, apply the same rounding logic. Report these values to the same number of decimal places as the mean or to one additional place at most.

Error 4: Figure Legends That Lack Experimental Context

A figure legend must stand alone. The reader should be able to understand what the figure shows without reading the full methods section. This means the legend must state the experimental conditions, the sample size, and the treatment groups being compared.

A legend that reads "Results of the experiment" provides no useful information. A legend that reads "Mean bacterial growth at 37 degrees C in LB broth with and without ampicillin. Error bars represent standard deviation from three independent replicates" tells the reader everything needed to interpret the figure.

The level of detail required depends on the complexity of the experiment. Simple experiments need shorter legends. Complex experiments with multiple variables need more complete legends. In every case, the legend should answer the basic questions of what was measured, under what conditions, and with how many replicates.

Error 5: Cluttered Figures With Too Many Data Series

A figure that tries to show too much information ends up showing nothing clearly. When you plot many treatment groups on a single graph, the individual data series become difficult to distinguish. Lines cross and overlap. Symbols become confused. The reader cannot extract the pattern you intended to demonstrate.

The solution is to simplify. If you have many treatment groups, consider whether you need to show all of them in a single figure. Often, the key comparison involves only two or three groups. The remaining groups can appear in a supplementary table or in a separate figure panel.

When multiple series are necessary, use distinct symbols and line styles that remain distinguishable when the figure is printed in black and white. Color alone should never carry the information, because readers may print your report in grayscale or may have color vision deficiencies.

Error 6: Inconsistent Formatting Between Figures

A lab report with figures in different styles looks unprofessional and suggests carelessness. If figure one uses filled circles for the control group and figure two uses open squares for the control group, the reader must relearn the figure conventions with each new display.

Establish a consistent style before you begin making figures. Decide on font type and size for axis labels. Decide on symbol styles for treatment groups. Decide on line thickness and style. Apply these decisions to every figure in the report.

The same consistency applies to tables. Use the same border style, header formatting, and alignment conventions throughout the report. This consistency makes the report easier to read and signals that you paid attention to detail.

Error 7: Misleading Axis Scaling

The scale of your axes determines the visual impression your data creates. A graph that starts at zero shows the full magnitude of the measured values. A graph that starts at a nonzero value exaggerates small differences between groups.

Neither approach is inherently wrong. The choice depends on what you want to emphasize. However, the choice must be deliberate and transparent. If you start an axis at a nonzero value, the reader should be able to see that the axis does not start at zero. A break in the axis or a clear indication of the starting value prevents misinterpretation.

The more serious problem occurs when axis scaling distorts the actual relationship in the data. If your control group has a mean of 10 and your treatment group has a mean of 12, a graph starting at 10 makes the difference appear dramatic. A graph starting at zero shows the difference as the modest 20 percent change it actually represents. Choose the scale that honestly represents your results.

Error 8: Raw Data Without Summary Statistics

A table that lists every individual measurement from every replicate quickly becomes overwhelming. If you measured 20 samples in triplicate, a table with 60 individual values forces the reader to do the statistical work themselves. The reader wants to see the pattern, not the raw numbers.

Present summary statistics instead. Report the mean and a measure of variability for each group. The measure of variability can be standard deviation, standard error, or a confidence interval, depending on the conventions of your field and the requirements of your assignment.

Raw data can appear in an appendix if your instructor requires it. The main text of the report should present summarized data that the reader can interpret at a glance.

Error 9: No Indication of Variability

A figure that shows only the mean of each group without any indication of variability tells the reader nothing about the reliability of the results. Two groups with identical means could have completely different distributions. One could have tightly clustered values. The other could have values spread across a wide range.

Error bars provide this information. Standard deviation bars show the spread of the individual measurements. Standard error bars show the precision of the estimated mean. Confidence intervals show the range within which the true mean likely falls.

Whichever measure you choose, the figure legend must state what the error bars represent. A reader cannot interpret error bars without knowing whether they show standard deviation or standard error. The choice affects the interpretation of the data.

Error 10: Figures and Tables Not Referenced in the Text

Every figure and table in your report must be introduced in the narrative text. The introduction should tell the reader what to look for in the display and what conclusion the display supports. A figure that appears without any textual reference leaves the reader wondering why it was included.

The standard pattern is to reference the figure or table in the text before it appears. The text should describe the key pattern visible in the display. The figure or table then provides the supporting evidence for that description.

For example, the text might say "The treatment group showed significantly higher growth than the control group (Figure 1)." The reader then looks at Figure 1 to verify this claim. Without the textual reference, the figure floats in the report without purpose.

How to Build a Clear Table

Step 1: Decide What the Table Must Communicate

Before you create a table, decide what information the reader needs to extract from it. A table should answer a specific question. If you cannot state what question the table answers, the table probably does not need to exist.

Tables work best for presenting exact numerical values that would be difficult to read from a graph. They also work well for presenting multiple variables measured on the same samples. A table that simply repeats information already visible in a figure wastes space.

Step 2: Structure the Table Logically

The typical structure places independent variables in the left column and dependent variables in the remaining columns. Each row represents one experimental group or one time point. Each column represents one measured variable.

The table title should describe the contents without repeating the column headers. A title that reads "Table 1. Mean growth rates of bacterial cultures under different temperature conditions" tells the reader what the table contains. The column headers then provide the specific values.

Step 3: Format for Readability

Use horizontal lines to separate the header from the data and to close the table. Avoid vertical lines, which create visual clutter. Align numbers by the decimal point so that the reader can scan down a column easily.

Keep the table on a single page when possible. A table that breaks across pages forces the reader to flip back and forth. If the table must continue across a page break, repeat the header row on the new page.

How to Build a Clear Figure

Step 1: Choose the Right Figure Type

The type of figure you choose depends on the nature of your data. A scatter plot shows the relationship between two continuous variables. A bar graph shows comparisons between discrete categories. A line graph shows trends over time or across a continuous variable.

Choosing the wrong figure type obscures your data. A bar graph for continuous data forces the reader to mentally reconstruct the trend. A line graph for categorical data implies a continuous relationship that does not exist.

Step 2: Design for the Reader

The reader should be able to extract the main pattern from your figure within seconds. This means the figure must be simple, uncluttered, and clearly labeled. Every element in the figure should serve a purpose. Decorative elements that do not communicate data should be removed.

Font sizes should be large enough to read without magnification. Axis labels should be legible. Symbols should be distinct. The figure should be sized to fit the page width without requiring the reader to squint.

Step 3: Write a Complete Legend

The legend accompanies the figure and provides the context the figure itself cannot show. State what was measured, under what conditions, and with what sample size. State what the error bars represent. Define any abbreviations used in the figure.

The legend should be placed below the figure, following the convention used in most scientific journals. The legend should be written in complete sentences, not fragments.

Common Failure Patterns in Student Lab Reports

The Spreadsheet Dump

The spreadsheet dump occurs when a student copies an entire data table from spreadsheet software directly into the report. The resulting table contains every intermediate calculation, every replicate, and every formula output. The reader cannot distinguish the key results from the supporting calculations.

The fix is to extract only the summary statistics needed to support your conclusions. Present the mean and variability for each group. Move the full data table to an appendix if your instructor requires it.

The Decorative Figure

The decorative figure looks impressive but communicates nothing. It might be a three dimensional bar graph with gradient fills and drop shadows. It might be a pie chart with twelve slices in rainbow colors. The visual effects distract from the data instead of clarifying it.

The fix is to remove all decorative elements. Use simple two dimensional graphs. Use color only to distinguish data series, and ensure the figure remains interpretable without color.

The Invisible Methods

The invisible methods problem occurs when the figure legend does not state the experimental conditions. The reader sees data points but cannot determine what was measured or under what conditions. The figure becomes meaningless without access to the methods section.

The fix is to write complete legends that state the essential experimental details. The legend should allow the reader to understand the figure without referring to the methods section.

The Inconsistent Report

The inconsistent report uses different formatting conventions in different figures and tables. One figure uses blue circles for the control group. Another uses red squares. One table centers its headers. Another left aligns them. The report looks like it was assembled from unrelated fragments.

The fix is to create a style guide before you begin making figures and tables. Decide on the conventions and apply them consistently throughout the report.

Records and Measurements That Support Good Data Presentation

Keep a Laboratory Notebook That Supports Your Figures

The quality of your figures depends on the quality of your records. A laboratory notebook that records the exact conditions of each measurement allows you to write complete figure legends. A notebook that records only the final values leaves you guessing at the conditions weeks later when you write the report.

Record the instrument used for each measurement. Record the settings on that instrument. Record the environmental conditions that might affect the measurement. Record the exact time and date of each measurement. These details become the basis for your figure legends and table footnotes.

Track Your Data Processing Steps

When you process raw data into summary statistics, keep a record of each step. Note which values were averaged. Note which calculations were applied. Note any data points that were excluded and the reason for exclusion.

This record serves two purposes. It allows you to verify your calculations when you review the report. It also provides the information you need to describe your data processing in the methods section.

Document Your Figure Design Decisions

When you choose an axis scale or a figure type, record the reasoning behind the choice. If you start an axis at a nonzero value, note why. If you choose a bar graph over a line graph, note why. These notes help you explain your choices if a grader questions them.

How to Review Your Own Report Before Submission

Step 1: Check Every Figure and Table Against the Checklist

Go through your report and examine each figure and table against the ten errors listed above. Check that every axis has a label with units. Check that every table header includes units. Check that decimal places match instrument precision. Check that legends state experimental conditions.

Step 2: Read the Report as a Stranger

Put yourself in the position of a reader who has not performed your experiment. Read the report without referring to your laboratory notebook. Can you understand what was done and what was found? Are there gaps in the presentation that force you to guess?

This exercise often reveals assumptions you made that the reader cannot share. You know what your abbreviations mean. You know what your experimental conditions were. The reader does not. The report must supply that information.

Step 3: Verify That Every Figure and Table Is Referenced

Search your text for each figure and table number. Every display should be introduced in the narrative before it appears. If a figure or table is never referenced in the text, either add a reference or remove the display.

Step 4: Check Formatting Consistency

Examine all figures and tables together. Are the fonts consistent? Are the symbol styles consistent? Are the table formats consistent? Apply the same conventions throughout.

Professional Context and Publication Ethics

Reporting Guidelines Apply Beyond the Classroom

The principles of clear data presentation that earn points in a lab report also apply to professional publication. The EQUATOR Network provides access to reporting guidelines used by journals across many fields. These guidelines exist to ensure that published research reports contain the information readers need to evaluate the work.

When you submit a manuscript to a journal, the editors will check your figures and tables against the journal's requirements. A manuscript with unclear figures may be returned for revision or rejected. The habits you develop in lab reports carry directly into professional practice.

Data Management and Sharing Expectations

Funding agencies increasingly expect researchers to manage and share their data according to structured plans. The National Institutes of Health Data Management and Sharing Policy describes expectations for data management planning and sharing in NIH funded research. These expectations include describing how data will be preserved and shared.

Clear data presentation in your reports supports these broader data management goals. When your figures and tables clearly show what you measured and found, your data becomes more useful to other researchers who might build on your work.

Authorship and Integrity

The Committee on Publication Ethics Core Practices describe expected behaviors for researchers, including standards for authorship, data integrity, and handling of misconduct. These practices apply to how you present data in any research context.

Presenting data honestly means not manipulating figures to exaggerate effects. It means not omitting data points that contradict your conclusions. It means describing your methods accurately so that others can reproduce your work. These standards apply whether you are writing a course lab report or a manuscript for a professional journal.

Researcher Identity and Record Keeping

Maintaining a clear record of your research activities supports your development as a scientist. ORCID provides researchers with a persistent identifier that connects their research outputs across their career. Building a record of well presented research reports and publications helps you establish your scientific identity.

The skills you practice in presenting data clearly in lab reports become part of your professional reputation. Researchers who present their work clearly are easier to collaborate with and more likely to have their work trusted.

Limitations of Standardized Formatting Rules

Different Fields Have Different Conventions

The formatting rules described in this article follow common conventions in the biological and physical sciences. Other fields may use different conventions. A psychology report may use American Psychological Association style. A chemistry report may follow a specific journal's format. An engineering report may use different figure conventions.

Always check the specific requirements of your assignment or target journal. The general principles of clarity and honesty apply everywhere. The specific formatting details may vary.

Course Requirements Override General Advice

Your instructor may have specific requirements that differ from the general advice in this article. Some instructors require raw data in the main text. Some require specific figure types. Some have specific formatting templates.

Follow your instructor's requirements. If a requirement conflicts with the general advice here, the instructor's requirement takes precedence. When in doubt, ask for clarification before you submit.

Software Limitations

The software you use to create figures may have limitations that affect your design choices. Some spreadsheet programs create default charts that are cluttered and difficult to read. You may need to manually adjust the default output to create a clear figure.

Learning to create publication quality figures requires practice. The default settings in most software packages do not produce ideal figures. You must learn to adjust axis scales, remove gridlines, resize fonts, and simplify the output.

When to Seek Help

Ask Your Instructor Before Submission

If you are unsure whether your figures and tables meet the assignment requirements, ask your instructor before the submission deadline. Most instructors are willing to review a draft or answer specific questions about formatting expectations. Asking before submission is always better than losing points after submission.

Use University Writing Centers

Many universities offer writing centers that help students with scientific writing. These centers can review your report and provide feedback on clarity and organization. They can help you identify places where your data presentation is unclear.

Consult Reporting Guidelines for Professional Work

If you are preparing a manuscript for professional publication, consult the reporting guidelines relevant to your field through the EQUATOR Network. These guidelines describe the information that must be included in published reports. Following them improves your chances of successful publication.

Frequently Asked Questions

How do I know how many decimal places to use in my tables?

Match the decimal places to the precision of your measurement instrument. If your instrument measures to the nearest 0.1 unit, report values to one decimal place. Calculated statistics such as means and standard deviations should be rounded to match the raw data precision or to one additional place at most.

Should I put raw data or summary statistics in my results section?

Present summary statistics such as means and measures of variability in the main results section. Raw individual measurements can be placed in an appendix if your instructor requires them. The reader needs to see the pattern in your data, not every individual value.

What should a figure legend include?

A figure legend should state what was measured, under what conditions, and with what sample size. It should define any abbreviations and state what error bars represent. The legend should allow the reader to understand the figure without reading the full methods section.

Is it acceptable to start a graph axis at a nonzero value?

Starting an axis at a nonzero value is acceptable when you clearly indicate that the axis does not start at zero. This practice can be useful for showing small differences between groups. However, it can also exaggerate the visual impression of differences. Choose the scale that honestly represents your results and make the axis range transparent to the reader.

How many data series can I include in one figure?

Include only as many data series as the reader can distinguish clearly. If series begin to overlap and become confused, separate them into multiple panels or move some data to a table. A figure that is cluttered communicates nothing clearly.

What is the difference between standard deviation and standard error bars?

Standard deviation bars show the spread of individual measurements around the mean. Standard error bars show the precision of the estimated mean. Confidence intervals show the range within which the true mean likely falls. The figure legend must state which measure you used because the interpretation differs.

Should I use color in my figures?

Color can be useful for distinguishing data series, but the figure must remain interpretable without color. Readers may print your report in grayscale or may have color vision deficiencies. Use distinct symbols and line styles in addition to color so that the figure works in any format.

Where should I place figures and tables in my report?

Follow your instructor's requirements. Some instructors want figures and tables placed within the text near where they are referenced. Others want them collected at the end of the report. In either case, every figure and table must be referenced in the narrative text before it appears.

Using the Evidence

Related Articles

• Data Presentation in Research: Tables, Figures, and Text
• How to Present Data in Research Papers: Methods, Tables, and Figures
• Common Mistakes in Meta-Analysis and How to Avoid Them
• Common Limitations in Research: Examples and How to Address Them
• Experimental Units: Definition, Examples, and Common Mistakes

References and Further Reading

• Research Methods Resources. National Library of Medicine.
• EQUATOR Network. EQUATOR Network.
• Core Practices. Committee on Publication Ethics.
• NIH Grants and Funding. National Institutes of Health.
• ORCID for Researchers. ORCID.
• Data Management and Sharing Policy. National Institutes of Health.
• NCBI Literature Resources. National Center for Biotechnology Information.
• PubMed. National Library of Medicine.

This article is educational and does not replace institutional policy, professional advice, or applicable safety and regulatory requirements.