What Is the Results Section?

The results section of a lab report presents the evidence collected during an investigation.

It answers the question:

What happened during the experiment?

Results may include:

• numerical measurements
• qualitative observations
• data tables
• calculated values
• averages
• graphs
• photographs
• diagrams

The goal is to communicate the evidence clearly and accurately.

The results section generally focuses on what was observed or measured, rather than providing a detailed explanation of why it happened.

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From Experiment to Evidence

During an experiment, you may collect many individual measurements.

For example:

21.4 s, 20.9 s, 21.2 s, 17.8 s, 17.5 s, 17.9 s...

A reader may find a long list difficult to understand.

Scientific data presentation turns these measurements into an organized form.

A common process is:

Collect → Record → Organize → Process → Graph → Describe

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Raw Data

Raw data are the original measurements or observations collected during an investigation.

For example:

Trial 1: 42.1 s

Trial 2: 41.8 s

Trial 3: 42.4 s

These values have not yet been averaged or otherwise processed.

Raw data should be recorded carefully because it forms the original evidence from the experiment.

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Processed Data

Processed data are values calculated from raw data.

Examples include:

• mean
• percentage
• rate
• difference
• change
• ratio
• calculated speed
• calculated density

Suppose the raw measurements are:

42.1 s, 41.8 s, 42.4 s

Mean:

(42.1 + 41.8 + 42.4) ÷ 3

= 42.1 s

The value 42.1 s is processed data.

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Quantitative Data

Quantitative data consist of numerical measurements.

Examples:

• mass = 12.6 g
• temperature = 38.2°C
• time = 15.4 s
• distance = 2.50 m
• volume = 35.0 mL
• force = 4.8 N

Quantitative data should normally include appropriate units.

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Qualitative Data

Qualitative data describe characteristics that are observed rather than measured numerically.

Examples:

• solution changed from blue to colourless
• bubbles formed rapidly
• white solid appeared
• metal surface became darker
• solution became cloudy
• noticeable odour was detected, when safe to observe appropriately

Qualitative observations can provide important evidence even when they do not contain numbers.

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Recording Observations Objectively

Scientific observations should describe what was actually observed.

Weak:

The reaction looked weird.

Better:

Rapid bubbling occurred immediately after the reactants were mixed.

Weak:

The liquid became gross.

Better:

The colourless solution became cloudy and a white solid formed.

Scientific descriptions should be objective and specific.

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Data Tables

A data table organizes measurements into rows and columns.

Tables make it easier to:

• compare measurements
• identify repeated trials
• calculate averages
• recognize trends
• prepare data for graphing

A good data table should be understandable without needing a long explanation.

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Features of a Good Data Table

A scientific data table should usually include:

• descriptive title
• clearly labeled columns
• variable names
• units in headings
• consistent precision
• logically ordered data
• raw measurements where appropriate
• processed values where appropriate

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Example of a Poor Data Table

Problems include:

• unclear title
• unclear units
• unclear meaning of columns 1, 2, and 3
• unclear measurement being recorded

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Improved Data Table

Effect of Water Temperature on Dissolving Time

Now the reader can understand the data without guessing.

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Units Belong in Headings

Instead of writing:

it is generally clearer to place units in the headings:

This avoids unnecessary repetition and follows common scientific conventions.

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Independent and Dependent Variables in Tables

A useful convention is to place the independent variable in the first column.

For example:

Temperature is being deliberately changed.

Reaction time is being measured.

Therefore:

Temperature = independent variable

Reaction time = dependent variable

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Consistent Precision

Experimental measurements should be recorded with appropriate and consistent precision.

Poor presentation:

12.1, 13, 14.3762, 15.20

If all measurements were made using the same instrument, this inconsistent precision may be inappropriate.

A more consistent set might be:

12.1, 13.0, 14.4, 15.2

The appropriate number of decimal places depends on the instrument and measurement.

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Do Not Invent Precision

Suppose a ruler measures only to the nearest millimetre.

Reporting:

12.583746 cm

would suggest far more precision than the equipment can provide.

Similarly, if a stopwatch measurement is strongly limited by human reaction time, many calculator-generated decimal places in an average may not be meaningful.

Scientific communication should reflect the quality of the measurements.

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Calculating a Mean

Repeated trials are often summarized using a mean.

Suppose:

Trial 1 = 18.4 s

Trial 2 = 18.7 s

Trial 3 = 18.2 s

Mean:

Mean = (18.4 + 18.7 + 18.2) ÷ 3

Mean = 18.43... s

Depending on the precision of the original measurements, this might reasonably be reported as:

18.4 s

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Why Calculate Means?

Means can help:

• summarize repeated measurements
• reduce the influence of random variation
• make comparisons easier
• prepare data for graphing

However, a mean should not completely hide the raw data.

If three trials are:

20.1 s, 20.2 s, 46.8 s

the mean alone does not reveal that one measurement is very different.

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Anomalous Results

An anomalous result is a measurement that does not fit the general pattern of the other data.

For example:

Trial 1 = 21.2 s

Trial 2 = 21.5 s

Trial 3 = 37.8 s

Trial 4 = 21.3 s

The value:

37.8 s

appears unusual.

It may be an anomaly.

Do not automatically delete unusual results.

They should be identified and investigated.

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Presenting an Anomaly

A scientist might write:

Trial 3 produced a substantially greater reaction time than the other trials and was identified as a possible anomalous result.

The analysis or evaluation can later discuss possible causes.

The results section should preserve an honest record of the collected data.

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Choosing a Graph

Graphs make numerical relationships easier to see.

Different graph types are useful for different kinds of data.

Common scientific graphs include:

• line graphs
• scatter plots
• bar charts

The choice should depend on the type of variables and the purpose of the graph.

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Line Graphs

A line graph is useful when data have a meaningful continuous or ordered progression and the goal is to show how a quantity changes across that progression.

Examples include:

• temperature over time
• position over time
• plant height over several weeks

The points may be joined when the progression between them is meaningful.

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Scatter Plots

A scatter plot is especially useful when investigating the relationship between two numerical variables.

Examples:

• spring force vs extension
• temperature vs reaction rate
• mass vs volume
• pendulum length vs period

Individual data points are plotted, and an appropriate line or curve of best fit may be added.

This helps reveal the overall relationship without assuming every measurement is exact.

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Bar Charts

Bar charts are useful when the independent variable consists of separate categories.

For example:

The fertilizer types are categories rather than continuous numerical values.

A bar chart would therefore be appropriate.

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Choosing Between Bar and Scatter Graphs

Ask:

Is my independent variable categorical or numerical?

If it is categorical:

bar chart

may be appropriate.

If both variables are numerical and you are examining their relationship:

scatter plot

is often appropriate.

If the data show change across an ordered sequence such as time:

line graph

may be appropriate.

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Independent Variable on the x-Axis

In many experimental graphs:

independent variable → x-axis

dependent variable → y-axis

Suppose the investigation asks:

How does temperature affect reaction time?

Then:

x-axis:

Temperature (°C)

y-axis:

Reaction Time (s)

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Graph Titles

A useful graph title should describe the relationship being displayed.

Weak:

Graph

Better:

Temperature vs Reaction Time

Even better:

Effect of Temperature on Reaction Time

The reader should immediately understand what the graph represents.

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Labeling Axes

Each axis should identify:

variable + unit

For example:

x-axis:

Temperature (°C)

y-axis:

Reaction Time (s)

Avoid labels such as:

x

and:

y

unless those symbols are themselves the meaningful variables being investigated.

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Choosing a Scale

A good graph scale should:

• cover the entire data range
• use equal intervals
• make efficient use of the graph area
• be easy to read
• avoid distorting the data

For example, if temperatures range from:

20°C to 60°C

a scale marked:

20, 30, 40, 50, 60

may be sensible.

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Misleading Graph Scales

Graph scales can change how dramatic a difference appears.

Suppose two values are:

98

and:

100

A y-axis ranging from:

0 to 100

shows a small difference.

A y-axis ranging from:

97 to 100

makes the difference appear visually much larger.

A shortened axis is not automatically wrong, but it should be used transparently and interpreted carefully.

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Plotting Data Accurately

Suppose the data are:

Each pair becomes a point:

(20, 83.7)

(30, 61.3)

(40, 43.0)

(50, 31.0)

(60, 24.0)

The graph allows the relationship to be seen much more quickly than the raw numbers alone.

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Lines of Best Fit

Experimental measurements rarely lie perfectly along a mathematical line.

Instead of connecting every point with a jagged path, it is often better to draw a line or curve of best fit when investigating a relationship between numerical variables.

The best-fit line represents the overall trend in the data.

It does not need to pass through every point.

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Interpolation

A graph can sometimes be used to estimate a value within the measured range.

Suppose measurements were collected at:

20°C, 30°C, 40°C, 50°C

We might use the trend to estimate a value at:

35°C

This is called interpolation.

Interpolation is generally more reliable than predicting far beyond the measured range.

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Extrapolation

Extrapolation means predicting beyond the range of measured data.

Suppose data were collected between:

20°C and 50°C

Using the trend to predict the result at:

100°C

would be extrapolation.

The relationship may change outside the measured range, so extrapolated predictions should be treated cautiously.

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Error Bars and Uncertainty

More advanced scientific graphs may include error bars.

Error bars can represent quantities such as:

• measurement uncertainty
• standard deviation
• standard error
• range

The report should explain what the error bars represent.

Never assume all error bars mean the same thing.

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Describing a Pattern

Once the data have been organized, the results can be summarized.

Suppose:

A simple summary is:

As temperature increased, reaction time decreased.

This identifies the overall pattern.

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Make Pattern Statements Quantitative

A stronger statement includes numerical evidence:

As temperature increased from 20°C to 50°C, mean reaction time decreased from 80 s to 36 s.

This is more informative because it identifies both:

the trend

and:

the evidence supporting it

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Positive Relationships

A positive relationship occurs when one variable tends to increase as another increases.

For example:

As force applied to a spring increases:

extension increases

This can be described as:

There was a positive relationship between applied force and spring extension.

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Negative Relationships

A negative relationship occurs when one variable tends to decrease as another increases.

For example:

As temperature increases:

reaction time may decrease

A suitable description might be:

There was a negative relationship between temperature and reaction time over the tested range.

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No Clear Relationship

Sometimes the data show no obvious trend.

That is still a valid result.

For example:

No clear relationship was observed between the independent variable and the measured response over the range tested.

Do not invent a trend simply because you expected one.

Scientific reports must represent the evidence honestly.

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Linear and Nonlinear Patterns

A relationship may be approximately linear.

This means the data follow approximately a straight-line pattern.

Other relationships are nonlinear.

They may:

• curve upward
• curve downward
• level off
• increase rapidly
• decrease rapidly

Describe the pattern actually shown by the data.

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Presenting Data vs Interpreting Data

This distinction is extremely important.

Presenting data tells the reader what was measured or observed.

Interpreting data explains what those measurements mean scientifically.

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Example: Presenting Data

The mean reaction time decreased from 82.4 s at 20°C to 31.2 s at 50°C.

This reports the pattern in the measurements.

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Example: Interpreting Data

The decrease in reaction time indicates that the reaction rate increased as temperature increased. This can be explained by particles having greater average kinetic energy at higher temperatures.

This moves beyond presentation and provides scientific interpretation.

The detailed explanation belongs primarily in the analysis or discussion section.

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Another Example

Observation:

The solution changed from colourless to pink.

Data summary:

The colour change occurred after 24.3 s.

Interpretation:

The colour change indicated that the reaction had reached the chosen endpoint.

Explanation:

The endpoint occurred because the chemical composition of the solution had changed sufficiently for the indicator to change colour.

These statements perform different roles.

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Results vs Analysis

A useful rule is:

Results = What happened?

Analysis = What does it mean?

Results may contain:

• measurements
• observations
• tables
• graphs
• calculated values
• concise descriptions of patterns

Analysis may contain:

• scientific explanations
• comparisons with theory
• explanations of anomalies
• discussion of relationships
• interpretation of significance

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Results vs Conclusion

The results section presents evidence.

The conclusion uses that evidence to answer the research question.

For example:

Result:

The mean extension increased from 1.2 cm at 1 N to 6.0 cm at 5 N.

Conclusion:

Over the tested range, increasing applied force increased spring extension, supporting the predicted relationship between force and extension.

The conclusion draws together the evidence into an answer.

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Scientific Conventions

Scientific conventions help make data understandable to other people.

Important conventions include:

• SI units where appropriate
• correct unit symbols
• clear variable names
• appropriate decimal places
• appropriate significant figures
• descriptive table titles
• labeled graph axes
• units in headings
• consistent formatting
• honest reporting of anomalies
• appropriate graph type

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Correct Unit Symbols

Examples include:

metre = m

second = s

kilogram = kg

newton = N

joule = J

degree Celsius = °C

millilitre = mL

Scientific unit symbols are standardized.

For example:

5 kg

not:

5 kgs

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Significant Figures

Significant figures help communicate the precision of measurements.

Consider:

12.0 cm

The final zero communicates information about measurement precision.

This is different from simply writing:

12 cm

In scientific work, digits should reflect the precision justified by the measurement.

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Scientific Notation in Results

Very large and very small values may be easier to communicate using scientific notation.

For example:

0.0000048 m

can be written:

4.8 × 10⁻⁶ m

And:

320,000,000 m

can be written:

3.2 × 10⁸ m

Scientific notation makes scale and significant figures easier to recognize.

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Example: Complete Results Presentation

Suppose a student investigates how force affects spring extension.

Raw data:

A concise results statement might be:

Mean spring extension increased from 2.0 cm at 1.0 N to 10.0 cm at 5.0 N. Over the tested range, extension increased by approximately 2.0 cm for each additional newton of applied force.

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Example: Qualitative Results

Suppose a student investigates a chemical reaction.

Observations:

Before mixing:

• Solution A was colourless.
• Solution B was pale blue.

After mixing:

• The mixture immediately became cloudy.
• A blue solid formed.
• The solid gradually settled at the bottom of the test tube.

This is clear qualitative data.

The student should not immediately explain the chemistry in the observations section.

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Example: Quantitative and Qualitative Data Together

Many investigations produce both types of evidence.

For example:

Quantitative:

Temperature increased from:

22.1°C to 31.8°C

Qualitative:

The container felt warmer and bubbling was observed.

Both observations can be reported.

The numerical temperature measurement provides stronger quantitative evidence of the temperature change.

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Worked Example 1: Improving a Table

Poor headings:

Improved:

Effect of Water Temperature on Dissolving Time

The improved table identifies:

• variables
• units
• context

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Worked Example 2: Choosing a Graph

Data:

Plant species A, B, C, and D

Measured variable:

Mean leaf length

The independent variable consists of categories.

Appropriate choice:

Bar chart

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Worked Example 3: Choosing a Graph

Data:

Time = 0, 1, 2, 3, 4, 5 minutes

Measured variable:

Temperature

The goal is to show how temperature changes over time.

Appropriate choice:

Line graph

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Worked Example 4: Choosing a Graph

Data:

Mass = 10, 20, 30, 40, 50 g

Measured variable:

Volume

The goal is to examine the relationship between two numerical variables.

Appropriate choice:

Scatter plot with an appropriate best-fit line

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Worked Example 5: Summarizing a Pattern

Data:

10°C → 112 s

20°C → 81 s

30°C → 58 s

40°C → 42 s

Weak:

The numbers went down.

Better:

Reaction time decreased as temperature increased.

Strong:

As temperature increased from 10°C to 40°C, reaction time decreased from 112 s to 42 s.

The final statement communicates the trend using evidence.

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Worked Example 6: An Anomaly

Results:

1 N → 2.1 cm

2 N → 4.0 cm

3 N → 9.7 cm

4 N → 8.1 cm

5 N → 10.0 cm

The value at:

3 N

does not fit the overall pattern.

A suitable results statement is:

Extension generally increased with force, although the 9.7 cm measurement at 3 N did not follow the overall trend and may represent an anomalous result.

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Worked Example 7: Avoiding Interpretation

Suppose a plant under blue light grows more than a plant under green light.

Results statement:

Mean plant growth was 8.2 cm under blue light and 4.6 cm under green light.

This is appropriate.

Statement:

Blue light caused greater growth because chlorophyll absorbs blue wavelengths efficiently.

This is scientific interpretation and belongs primarily in the analysis.

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Worked Example 8: Communicating Change

Initial mass:

25.4 g

Final mass:

22.1 g

Change:

22.1 − 25.4 = −3.3 g

A clear statement is:

The sample's mass decreased by 3.3 g, from 25.4 g to 22.1 g.

This is clearer than simply writing:

Change = −3.3

because the quantity and unit are identified.

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Error Analysis

A student creates this graph:

Title:

Graph

x-axis:

Numbers

y-axis:

Results

Even if the points are plotted correctly, the graph is difficult to interpret.

Better:

Title:

Effect of Temperature on Reaction Time

x-axis:

Temperature (°C)

y-axis:

Reaction Time (s)

Scientific graphs should be understandable without guessing what the axes represent.

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Another Error Analysis

A student writes:

The reaction got faster because increasing temperature increased the frequency and energy of particle collisions.

This may be a reasonable scientific explanation.

However, if the task is specifically to present the results, the student has moved into interpretation.

A results statement would be:

Mean reaction time decreased from 75.2 s at 20°C to 29.8 s at 50°C.

The collision explanation belongs in the analysis.

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Another Error Analysis

A student obtains:

15.2 cm, 15.4 cm, 32.8 cm

and reports only:

Mean = 21.1 cm

This hides important information.

The raw measurements show that:

32.8 cm

may be anomalous.

Good data presentation should make important variation visible rather than hiding it behind an average.

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A Reliable Results-Writing Strategy

When preparing your results:

Step 1: Preserve the raw data.

Do not rely on memory.

Step 2: Organize the data.

Use a clearly labeled table.

Step 3: Check units and precision.

Make formatting consistent.

Step 4: Process the data where appropriate.

Calculate means, rates, percentages, or other required quantities.

Step 5: Look for unusual results.

Identify possible anomalies.

Step 6: Choose an appropriate graph.

Consider the types of variables.

Step 7: Label the graph completely.

Include title, axes, and units.

Step 8: Describe the overall pattern.

State what increases, decreases, remains constant, or shows no clear relationship.

Step 9: Support the pattern with numbers.

Use specific evidence.

Step 10: Save detailed scientific explanations for the analysis.

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Results Checklist

Before completing the results section, ask:

• Have I included all relevant data?
• Are the raw measurements preserved?
• Are tables clearly titled?
• Are variables clearly labeled?
• Are units included in headings?
• Is precision consistent?
• Have calculations been performed correctly?
• Are means appropriate?
• Are possible anomalies visible?
• Is the graph type appropriate?
• Is the independent variable on the appropriate axis?
• Are both axes labeled?
• Are units shown?
• Is the graph scale sensible?
• Are points plotted accurately?
• Is a best-fit line or curve appropriate?
• Have I described the overall trend?
• Have I included numerical evidence?
• Have I avoided detailed explanations that belong in the analysis?

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Did You Know?

A well-designed graph can reveal patterns that are difficult to recognize in a table containing dozens or hundreds of measurements.

However, graphs can also hide or exaggerate patterns if scales, axes, or graph types are chosen poorly.

This is why scientific data presentation is not simply about making results look attractive.

It is about communicating evidence accurately, efficiently, and honestly.

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Key Terms

• Results: Evidence collected during an investigation.
• Raw data: Original measurements or observations before processing.
• Processed data: Values calculated from raw measurements.
• Quantitative data: Numerical measurements.
• Qualitative data: Descriptive observations.
• Data table: Organized arrangement of measurements in rows and columns.
• Mean: Sum of values divided by the number of values.
• Anomaly: Result that does not fit the general pattern.
• Independent variable: Variable deliberately changed.
• Dependent variable: Variable measured or observed.
• Bar chart: Graph useful for comparing separate categories.
• Line graph: Graph useful for showing change across a meaningful continuous or ordered progression.
• Scatter plot: Graph showing the relationship between two numerical variables.
• Line of best fit: Line representing the overall pattern of plotted data.
• Interpolation: Estimation within the measured data range.
• Extrapolation: Prediction beyond the measured data range.
• Error bar: Graphical representation of variation or uncertainty.
• Trend: Overall pattern in data.
• Positive relationship: One variable tends to increase as another increases.
• Negative relationship: One variable tends to decrease as another increases.
• Scientific convention: Standard practice used to communicate scientific information consistently.

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Key Relationships

Experimental evidence moves through the sequence:

Raw Data → Organized Table → Processed Data → Graph → Pattern

For many experimental graphs:

Independent variable → x-axis

Dependent variable → y-axis

A strong pattern statement combines:

Trend + Numerical Evidence

For example:

As X increased, Y decreased from ___ to ___.

Scientific reporting separates:

Results = What happened?

from:

Analysis = What does it mean?

and:

Conclusion = What answer does the evidence support?

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Key Takeaways

• The results section presents evidence collected during an investigation.
• Results may include quantitative and qualitative data.
• Raw data should be preserved.
• Processed data are calculated from raw measurements.
• Tables organize experimental data efficiently.
• Good tables have descriptive titles, clear headings, units, and consistent formatting.
• The independent variable is commonly placed in the first column of a data table.
• Units should normally appear in table headings rather than being repeated in every cell.
• Measurement precision should reflect the equipment and procedure used.
• Calculator output should not automatically determine the number of decimal places reported.
• Repeated measurements can be summarized using means when appropriate.
• Raw data should remain visible when variation or anomalies are important.
• Anomalous results should be reported honestly rather than automatically deleted.
• Graph type should match the type of data.
• Bar charts are useful for comparing categories.
• Line graphs are useful for showing change across a meaningful ordered progression such as time.
• Scatter plots are useful for investigating relationships between numerical variables.
• Independent variables are commonly placed on the x-axis.
• Dependent variables are commonly placed on the y-axis.
• Graph axes require variable names and units.
• Graph scales should be clear, consistent, and not misleading.
• A line or curve of best fit can show the overall pattern in experimental data.
• Interpolation estimates values within the measured range.
• Extrapolation predicts beyond the measured range and should be treated more cautiously.
• Error bars can communicate uncertainty or variation, but their meaning must be identified.
• Results should summarize important patterns.
• Strong pattern statements use specific numerical evidence.
• Positive, negative, linear, nonlinear, and absent relationships should be described accurately.
• Presenting data is different from interpreting data.
• Results primarily communicate what happened.
• Analysis explains what the results mean scientifically.
• Conclusions use the evidence to answer the research question.
• SI units, correct symbols, significant figures, scientific notation, clear labels, and appropriate precision are important scientific conventions.
• Effective data presentation is not about making data look impressive; it is about communicating evidence clearly, accurately, and honestly.