How to Report a Science Investigation That Doesn’t Support Your Hypothesis
Learn how to report a science investigation when your results contradict your hypothesis. Understand how to separate observations, interpretations, and limitations, evaluate unexpected results, and write an evidence-based conclusion.
Your pendulum is twice as long, but it does not take twice as long to swing. You’ve repeated the timing, checked the measurements, and the results still disagree with your hypothesis. How should you report that?
Keep the measurements exactly as recorded. State clearly whether the evidence supports your original hypothesis, then explain what the results suggest and where the investigation falls short. An investigation is not a failure because its hypothesis wasn’t supported.
In fact, an unexpected result can be the most useful part of an investigation. The important thing is to report it honestly and draw conclusions that the evidence can actually justify.
01Separate your observations, interpretations, and limitations
Before writing your discussion, imagine that someone has handed you the results without telling you what the original hypothesis was.
What can you actually see in the data?
That is your starting point. A scientific report needs to distinguish three different things:
| Part of the report | What it means | Example |
|---|---|---|
| Observation | What you measured or recorded | The mean pendulum period increased from 1.01 s to 2.00 s as its length increased from 25 cm to 100 cm. |
| Interpretation | What the observations suggest | Increasing the pendulum length increases its period, but the relationship is not directly proportional. |
| Limitation | Something that restricts the accuracy, reliability, or scope of your conclusion | Hand-operated timing introduces reaction-time uncertainty. |
These three parts have different jobs.
1. Observations: what actually happened
Observations belong in your results section.
They include:
- Raw measurements from individual trials.
- Qualitative observations, such as colour changes, gas production, or changes in appearance.
- Processed data, such as calculated averages, rates, and percentages.
- Patterns identified in tables and graphs.
Your observations should describe what happened without trying to explain why.
For example:
“The mean reaction time decreased as the concentration of hydrochloric acid increased.”
This describes a pattern.
By contrast:
“The higher acid concentration caused more frequent successful collisions between particles.”
This is an interpretation because it uses scientific theory to explain the pattern.
Both statements can belong in your report, but they belong in different parts.
2. Interpretation: what the evidence means
Your discussion section is where you compare the observations with your original prediction.
There are several possible outcomes:
- Supported: The results agree with the predicted relationship.
- Partially supported: Some aspects of the prediction agree with the results, but others do not.
- Not supported: The observed pattern does not agree with the prediction.
- Inconclusive: The investigation cannot distinguish between the possible outcomes with sufficient confidence.
Notice the difference between the last two.
If your results clearly contradict your prediction, the hypothesis is not supported.
If your measurements are too imprecise to identify a meaningful pattern, you may not have enough evidence to make a decision.
An inconclusive result is not the same as evidence that the hypothesis is false.
3. Limitations: what the investigation could not establish
A limitation is a weakness in the investigation that affects how confidently you can interpret the results.
Avoid vague statements such as:
“Human error may have affected the experiment.”
That tells the reader almost nothing.
Instead, identify the specific issue, explain its effect, and suggest an improvement.
For example:
“Reaction times were measured using a handheld stopwatch. The experimenter’s reaction time when starting and stopping the timer may have introduced uncertainty into each measurement. Using an electronic timing system would reduce this uncertainty.”
A useful limitation has three parts:
- What was the weakness?
- How could it have affected the results?
- What specific change would improve the investigation?
Also distinguish between random and systematic errors.
- Random errors cause measurements to vary unpredictably between trials.
- Systematic errors shift measurements consistently in one direction.
Repeating an experiment can reduce the influence of random variation, but it does not automatically correct a systematic error.
02How should you structure the report?
When your hypothesis isn’t supported, your report still follows a logical structure. The main difference is that your discussion and conclusion must explain the unexpected outcome rather than trying to justify the original prediction.
A practical structure is:
| Section | What to include |
|---|---|
| Aim | The scientific question being investigated. |
| Hypothesis | Your original, testable prediction or proposed relationship. |
| Method | The procedure, variables, equipment, and controls. |
| Results | Raw observations, processed data, tables, and graphs. |
| Discussion | Interpretation of the results, comparison with the hypothesis, and relevant scientific explanations. |
| Evaluation | Limitations, their effects, and realistic improvements. |
| Conclusion | A direct answer stating whether the results supported the hypothesis. |
Your teacher may combine some sections or require additional headings. Follow the assessment notification rather than assuming every investigation uses exactly the same format.
Writing the results section
The results section should present the evidence without manipulating it to match your hypothesis.
For quantitative investigations:
- Include the original measurements, not just calculated averages.
- Give every measurement its appropriate unit.
- Show relevant calculations and processed data.
- Include a correctly labelled graph where appropriate.
- Show repeated measurements and identify unusual results.
- Include uncertainty or variation where it is relevant to interpreting the pattern.
For qualitative investigations, describe what was observed as precisely as possible.
For example, write:
“A pale yellow precipitate formed after the solutions were mixed.”
Rather than:
“The reaction worked as expected.”
The first statement records an observation. The second makes an interpretation.
Writing the discussion section
The discussion should explain the relationship between your results and your hypothesis.
A useful sequence is:
First, identify the observed pattern.
Refer directly to the measurements or graph.
Second, compare it with the prediction.
Explain exactly which part of the hypothesis was supported or contradicted.
Third, explain the scientific significance.
Use relevant scientific concepts to explain the observed behaviour.
Finally, consider alternative explanations.
Could an uncontrolled variable, measurement limitation, or competing scientific model explain the outcome?
Don’t invent an explanation just because the result looks unusual. If you cannot identify the cause from your evidence, say so.
Writing the evaluation section
A strong evaluation does more than list possible errors.
It explains how each limitation affects the conclusions.
Consider these examples:
| Weak evaluation | Stronger evaluation |
|---|---|
| There may have been timing errors. | Manual timing introduced reaction-time uncertainty, which could affect the calculated period of each oscillation. |
| There weren’t enough experiments. | Only three trials were conducted at each concentration, limiting the ability to identify the extent of random variation. |
| The equipment wasn’t accurate. | The thermometer’s resolution was too low to distinguish the small temperature differences between treatments. |
| The method could be improved. | Using a calibrated digital temperature probe would allow smaller temperature changes to be measured. |
The improvement should address the limitation you identified.
If temperature was not controlled, collecting more trials without controlling temperature won’t solve the problem.
Writing the conclusion
Your conclusion should directly answer the original research question.
It should:
- Restate the hypothesis briefly.
- State whether the results supported it.
- Refer to the most relevant evidence.
- Qualify the conclusion according to the investigation’s limitations.
A useful sentence structure is:
“The results do not support the hypothesis that [original prediction]. The observations showed that [specific result], which differs from the predicted relationship. This suggests that [interpretation], although [relevant limitation] restricts the conclusions that can be drawn.”
You don’t need to pretend the experiment succeeded.
You need to demonstrate that you understand what the evidence shows.
03Worked example: a pendulum does not follow the predicted relationship
A student investigates the relationship between pendulum length and period.
Their hypothesis is:
“The period of a simple pendulum is directly proportional to its length.”
They predict that doubling the pendulum length will double the time taken for one complete oscillation.
The student measures the time taken for 10 oscillations at four different lengths, completing three trials at each length.
The following is an illustrative dataset.
| Length (cm) | Trial 1: 10 oscillations (s) | Trial 2 (s) | Trial 3 (s) | Mean time (s) |
|---|---|---|---|---|
| 25 | 10.0 | 10.2 | 10.1 | 10.1 |
| 50 | 14.1 | 14.0 | 14.2 | 14.1 |
| 75 | 17.2 | 17.4 | 17.3 | 17.3 |
| 100 | 19.9 | 20.1 | 20.0 | 20.0 |
The student calculates the period by dividing the mean time for 10 oscillations by 10.
Step 1
The period, \(T\), is the time taken for one complete oscillation.
\[ T=\frac{\text{Mean time for 10 oscillations}}{10} \]
For the 25 cm pendulum:
\[ T=\frac{10.1}{10}=1.01\text{ s} \]
The processed results are:
| Length (cm) | Period (s) |
|---|---|
| 25 | 1.01 |
| 50 | 1.41 |
| 75 | 1.73 |
| 100 | 2.00 |
Step 2
If the period were directly proportional to length, doubling the length from 25 cm to 50 cm should double the period.
The predicted period would therefore be:
\[ T=2(1.01)=2.02\text{ s} \]
However, the measured period is only 1.41 s.
The results do not match the prediction.
Step 3
The period increases as the pendulum becomes longer, but it does not increase in direct proportion to length.
This is an important distinction. The results do not show that pendulum length has no effect. They show that the proposed mathematical relationship is incorrect.
In fact, for a simple pendulum released at a small angle, the theoretical model is:
\[ T=2\pi\sqrt{\frac{L}{g}} \]
Here:
- \(T\) is the period in seconds.
- \(L\) is the pendulum length in metres.
- \(g\) is gravitational field strength in metres per second squared.
This model predicts that doubling the length increases the period by a factor of \(\sqrt{2}\), approximately 1.41.
The measured ratio is:
\[ \frac{1.41}{1.01}\approx1.40 \]
The observations are therefore consistent with the square-root relationship, rather than direct proportionality.
Step 4
Hand-operated timing introduces reaction-time uncertainty.
However, timing 10 oscillations and dividing by 10 reduces the effect of starting and stopping errors on the calculated period of one oscillation.
Other limitations include:
- Inaccurate measurement of the distance from the pivot to the centre of the bob.
- Variations in the initial release angle.
- Air resistance and friction at the pivot.
Using an electronic timing gate and measuring the pendulum length more carefully would improve the investigation.
Step 5
An appropriate conclusion would be:
“The results do not support the hypothesis that the period of a simple pendulum is directly proportional to its length. Although the period increased as the pendulum length increased, doubling the length from 25 cm to 50 cm increased the period from 1.01 s to 1.41 s rather than doubling it. This relationship is consistent with the square-root model for a simple pendulum at small angles. Therefore, the results indicate that pendulum period increases with length, but not in direct proportion.”
Notice what the student hasn’t done.
They haven’t changed the original hypothesis, removed inconvenient measurements, or claimed that the experiment proved a new scientific law.
They’ve identified exactly what the evidence supports.
04Worked example: increasing fertiliser concentration does not always increase plant growth
Now consider a Biology investigation.
A student predicts:
“Increasing fertiliser concentration will always increase the height of seedlings.”
They grow five seedlings at each of four fertiliser concentrations. All seedlings are the same species and starting age, and receive the same soil, water, and light exposure.
After 14 days, they calculate the mean height for each group.
The illustrative results are:
| Fertiliser concentration (g/L) | Mean height after 14 days (cm) |
|---|---|
| 0 | 5.0 |
| 1 | 6.1 |
| 2 | 6.3 |
| 4 | 4.9 |
Step 1
Mean seedling height increased from 5.0 cm to 6.3 cm as fertiliser concentration increased from 0 g/L to 2 g/L.
However, at 4 g/L, the mean height decreased to 4.9 cm.
Step 2
The hypothesis predicts a continuous increase in plant height as fertiliser concentration increases.
The results contradict this prediction at the highest concentration.
The hypothesis is therefore not supported as written.
Step 3
One possibility is that the highest fertiliser concentration created excessive salt concentrations around the roots, affecting water uptake.
However, the investigation did not measure soil salinity, water uptake, or nutrient toxicity.
It would be incorrect to present this explanation as an established cause.
Instead, it should be identified as a possible explanation requiring further investigation.
Step 4
Several limitations affect the conclusions:
- Only five seedlings were tested at each concentration.
- Reporting only mean heights hides variation between individual plants.
- Only four concentrations were investigated, so the precise concentration associated with maximum growth is unknown.
- Plant height alone does not measure total biomass or overall plant health.
A stronger investigation would use more seedlings, report the spread of individual measurements, and test additional concentrations between 2 g/L and 4 g/L.
Step 5
“The results do not support the hypothesis that increasing fertiliser concentration always increases seedling height. Mean height increased between 0 g/L and 2 g/L but decreased at 4 g/L. This suggests that the relationship between fertiliser concentration and seedling growth may not be continuously increasing. However, further investigation is required to determine the cause of the reduced growth at the highest concentration.”
This conclusion is more scientifically useful than simply writing that fertiliser does not work.
The results suggest that the relationship is more complicated than the original hypothesis proposed.
05What should you do when your results look wrong?
This is where students sometimes make their biggest reporting mistakes.
A result doesn’t become invalid just because it disagrees with the others.
You need to distinguish between an unusual measurement, a documented experimental error, and a result that genuinely contradicts your prediction.
Don’t delete an inconvenient result
Suppose you measure the time for a trolley to travel down a ramp three times:
\[ 2.10\text{ s},\quad 2.08\text{ s},\quad 3.45\text{ s} \]
The third measurement is noticeably different.
Should you remove it?
Not automatically.
First, check your experimental records.
If you documented that the trolley collided with the side of the track during the third trial, you have a specific reason to identify that trial as invalid for comparison.
You should:
- Keep the original measurement in your raw data.
- Explain the recorded procedural problem.
- Identify the trial as invalid.
- Exclude it from the processed mean only if the reason justifies doing so.
- Repeat the trial if possible and record the new measurement separately.
If you cannot identify a genuine procedural problem, keep the measurement and investigate the variation.
Never remove a result simply because it makes your graph look less convincing.
Don’t confuse an inconclusive result with a failed hypothesis
Imagine you are investigating whether increasing temperature raises the boiling point of a salt solution.
Your thermometer has an uncertainty of ±0.5 °C.
You obtain these mean results:
| Salt added (g per 100 mL) | Mean boiling temperature (°C) |
|---|---|
| 0 | 100.0 |
| 5 | 100.2 |
| 10 | 100.1 |
The results do not show a clear increase.
However, the differences are smaller than the thermometer’s stated uncertainty.
You cannot confidently distinguish the measured temperatures.
The correct conclusion is that the investigation is inconclusive with the measurement method used.
It would be incorrect to claim that salt concentration has no effect on boiling point.
A more precise, calibrated temperature probe and carefully controlled experimental conditions would help resolve the relationship.
Don’t rewrite the hypothesis after seeing the results
You may discover that a different relationship explains your observations more effectively.
That’s useful.
You can discuss an alternative explanation and propose a revised hypothesis for a future investigation.
However, your original hypothesis must remain unchanged in the report.
A hypothesis is a prediction made before examining the results. Changing it afterwards makes it difficult to distinguish a genuine prediction from an explanation constructed to fit the evidence.
06A decision process for writing your conclusion
When you’re unsure how to classify your results, use this sequence.
The key is to make the decision using the evidence, not your expectations.
07What does this mean for your NSW HSC science report?
There is an important difference between NESA course requirements and the specific instructions your school gives you.
NESA establishes syllabus outcomes and course assessment requirements. Schools prepare their assessment programs and set individual assessment tasks, including their due dates and weightings.
The NESA 2026 Rules and Procedures guide explains this distinction.
The structure in this article is a practical scientific reporting framework, not a universal NESA-mandated report template.
For your assessment, check your teacher’s task notification and marking criteria for:
- Required report headings.
- Word limits and formatting.
- Whether results and discussion should be separate.
- Required tables, graphs, and calculations.
- Referencing requirements.
- The specific syllabus outcomes being assessed.
Also check that you’re using the syllabus edition applicable to your cohort. As at November 2026, NESA lists the new Physics 11-12 syllabus for implementation from 2027 and the new Chemistry 11-12 syllabus for implementation from 2028. Their transition dates are different. The NESA science syllabuses page identifies the relevant editions.
If you’re completing a depth study, remember that the depth study is the broader investigation, while your report communicates the scientific work and findings. Read What Is a Depth Study in HSC Science? for that broader context.
Similarly, reporting your results is one part of the wider process of Working Scientifically. The related skills include planning investigations, processing data, analysing evidence, and communicating scientific arguments. You can explore these connections in What Is Working Scientifically in HSC Physics and Chemistry?.
08Questions and solutions
The following questions test whether you can distinguish an unsupported hypothesis from an inconclusive result, identify limitations, and construct conclusions that match the evidence.
Question 1
A student investigates whether increasing daily light exposure increases the height of bean seedlings.
The student predicts that seedlings receiving 8 hours of light per day will grow taller than seedlings receiving 4 hours.
Six seedlings are used in each group. Both groups receive the same soil, water, and starting conditions.
After 14 days, the mean heights are:
| Daily light exposure | Mean height (cm) |
|---|---|
| 4 hours | 6.0 |
| 8 hours | 5.4 |
Write an appropriate conclusion. Also explain one claim that the student cannot justify from these results.
Solution 1
The results do not support the hypothesis that 8 hours of daily light exposure produces taller seedlings than 4 hours under the conditions tested.
The mean height of the 8-hour group was 5.4 cm, compared with 6.0 cm for the 4-hour group.
The results show that the longer light exposure did not produce the predicted increase in mean height.
However, the student cannot conclude that increasing light exposure always reduces plant growth.
Only two light exposure durations were tested. The results also do not establish whether the difference between the groups is meaningful without considering the spread of individual measurements.
A suitable conclusion should therefore be restricted to the two conditions investigated.
Question 2
A student investigates how increasing ramp height affects the time taken for a trolley to travel 1.00 m down a ramp.
The hypothesis predicts that increasing ramp height will decrease travel time.
The student uses the same trolley, ramp, and release method for every trial. The trolley is released from rest without an initial push.
The following measurements are obtained:
| Ramp height (cm) | Trial 1 (s) | Trial 2 (s) | Trial 3 (s) |
|---|---|---|---|
| 10 | 2.10 | 2.06 | 2.08 |
| 20 | 1.48 | 1.51 | 1.49 |
| 30 | 1.10 | 1.12 | 1.95* |
During the third trial at 30 cm, the trolley collided with the side rail approximately halfway down the ramp.
No additional trial is possible.
Explain how the student should process the data, whether the hypothesis is supported, and how the unusual measurement should be reported.
Solution 2
The results support the hypothesis that increasing ramp height decreases travel time over the tested range. The 1.95 s measurement should remain in the raw data but should be excluded from the calculated mean because a documented collision affected that trial.
Step 1: Calculate the mean travel times.
For the 10 cm ramp:
\[ \bar{t}=\frac{2.10+2.06+2.08}{3} =2.08\text{ s} \]
For the 20 cm ramp:
\[ \bar{t}=\frac{1.48+1.51+1.49}{3} =1.49\text{ s} \]
For the 30 cm ramp, use the two valid trials:
\[ \bar{t}=\frac{1.10+1.12}{2} =1.11\text{ s} \]
The processed results are:
| Ramp height (cm) | Mean travel time (s) | Valid trials |
|---|---|---|
| 10 | 2.08 | 3 |
| 20 | 1.49 | 3 |
| 30 | 1.11 | 2 |
Step 2: Explain the treatment of the unusual result.
The 1.95 s reading is not removed simply because it differs from the other measurements.
The student’s experimental notes identify a collision with the side rail. This changes the trolley’s motion and means the trial was not conducted under the intended conditions.
The reading should remain visible in the raw data, with an explanation that it was excluded from the mean.
The student must also state that no replacement trial was conducted.
Step 3: Evaluate the reliability of the conclusion.
The mean travel time decreases as ramp height increases.
This supports the predicted relationship.
However, the 30 cm condition has only two valid trials. The smaller number of repetitions makes it harder to assess the variation at that height.
A repeat investigation should include additional valid trials at 30 cm.
Question 3
A student investigates whether increasing sodium chloride concentration increases the boiling point of water.
The student predicts that a higher salt concentration will produce a higher boiling temperature.
Three trials are conducted at each concentration, and the following mean temperatures are obtained:
| Sodium chloride added (g per 100 mL) | Mean boiling temperature (°C) |
|---|---|
| 0 | 100.0 |
| 5 | 100.2 |
| 10 | 100.1 |
The same volume of water is used in each experiment, and all trials are conducted in the same laboratory.
The thermometer has a stated measurement uncertainty of ±0.5 °C.
Is the hypothesis supported, not supported, or inconclusive? Justify your decision and suggest an improvement to the investigation.
Solution 3
The result is inconclusive because the observed temperature differences are smaller than the measurement uncertainty.
The thermometer has an uncertainty of ±0.5 °C.
The difference between the lowest and highest recorded mean temperatures is only:
\[ 100.2-100.0=0.2^\circ\text{C} \]
This difference is smaller than the thermometer’s stated uncertainty.
Therefore, the measurements cannot reliably distinguish the boiling temperatures of the three solutions.
The student should not conclude that increasing salt concentration has no effect. The equipment may simply be unable to resolve the difference.
A suitable conclusion would be:
“The investigation produced no clearly distinguishable increase in boiling temperature as sodium chloride concentration increased. However, the measurement uncertainty of ±0.5 °C was too large to determine whether small temperature differences existed. Therefore, the hypothesis could not be reliably evaluated using the equipment available.”
An improvement would be to use a calibrated digital temperature probe with greater precision.
The student should also maintain consistent heating conditions and repeat the investigation to assess variation between trials.
Question 4
A student investigates the effect of light intensity on the rate of photosynthesis in an aquatic plant.
The hypothesis predicts that increasing light intensity will increase the rate of photosynthesis.
The student places a lamp at different distances from the plant and counts the oxygen bubbles released per minute.
The mean results from three trials at each distance are:
| Lamp distance (cm) | Mean bubbles per minute | Water temperature (°C) |
|---|---|---|
| 40 | 8 | 20 |
| 30 | 13 | 22 |
| 20 | 18 | 25 |
| 10 | 12 | 30 |
The student concludes:
“Very high light intensity decreases the rate of photosynthesis.”
Evaluate this conclusion. Identify two limitations of the investigation and suggest improvements.
Solution 4
The student’s conclusion is not justified because the investigation cannot establish whether the decrease in bubble production was caused by increased light intensity or increased water temperature.
The results show that bubble production initially increased as the lamp moved closer:
- At 40 cm, the mean was 8 bubbles per minute.
- At 30 cm, it increased to 13 bubbles per minute.
- At 20 cm, it increased to 18 bubbles per minute.
However, at 10 cm, the mean decreased to 12 bubbles per minute.
This does not support a continuously increasing relationship between lamp proximity and bubble production.
Limitation 1: Water temperature was not controlled.
Moving the lamp closer increased both light intensity and water temperature.
These are two independent factors that could affect photosynthesis.
Higher temperatures can affect enzyme activity and may reduce photosynthetic performance if conditions become unsuitable.
Because both variables changed together, the investigation cannot isolate the effect of light intensity.
An improvement would be to maintain a constant water temperature using a controlled water bath or an appropriate light source that produces less heating.
Limitation 2: Bubble count is an indirect measurement of photosynthesis rate.
Counting bubbles assumes that each bubble contains approximately the same volume of oxygen.
However, bubble sizes may vary.
Consequently, 12 bubbles per minute does not necessarily represent less oxygen production than 18 bubbles per minute.
An improvement would be to collect oxygen using a gas syringe and measure the volume produced over a fixed period.
The student should also measure light intensity at the plant rather than relying solely on lamp distance.
A more defensible conclusion would be:
“Bubble production increased as the lamp moved closer from 40 cm to 20 cm, but decreased at 10 cm. The results do not support a continuously increasing relationship between lamp proximity and bubble production. However, because water temperature also increased as the lamp moved closer, the effect of light intensity could not be isolated.”
Question 5
A student investigates the effect of hydrochloric acid concentration on the reaction between magnesium and hydrochloric acid.
Their original hypothesis is:
“Increasing hydrochloric acid concentration will increase the total volume of hydrogen gas produced when it reacts with a fixed mass of magnesium.”
The student reacts 0.050 g of magnesium with 25.0 mL of hydrochloric acid at 25 °C.
The magnesium strips have the same exposed surface area, and hydrochloric acid is in excess at every concentration.
Hydrogen gas is collected using a gas syringe.
The following results are the means of three trials at each concentration:
| HCl concentration (mol/L) | Gas after 15 s (mL) | Gas after 30 s (mL) | Gas after 60 s (mL) | Final gas volume (mL) | Time until reaction stops (s) |
|---|---|---|---|---|---|
| 0.50 | 8 | 17 | 37 | 40 | 94 |
| 1.00 | 16 | 31 | 40 | 40 | 46 |
| 1.50 | 22 | 38 | 40 | 40 | 31 |
Assume that all magnesium reacts completely, the gas collection system has no leaks, and the volume measurements have an uncertainty of ±1 mL.
After seeing the results, the student argues:
“The higher acid concentrations clearly produced more hydrogen because the reactions finished faster.”
Another student argues:
“The final gas volumes were identical, so acid concentration had no effect on the reaction.”
Evaluate both arguments.
Your response should:
- Determine whether the original hypothesis is supported.
- Explain whether the results support a different hypothesis about reaction rate.
- Explain why the final gas volumes are the same.
- Identify the role of the limiting reagent.
- Explain how the original hypothesis and the alternative interpretation should appear in the report.
Solution 5
The original hypothesis is not supported, but the results support a different hypothesis: increasing hydrochloric acid concentration increases the reaction rate without increasing the final amount of hydrogen produced.
The key is to distinguish reaction rate from total product yield.
Step 1: Evaluate the original hypothesis.
The original hypothesis predicts that increasing hydrochloric acid concentration will increase the total volume of hydrogen produced.
However, all three concentrations produce a final gas volume of 40 mL.
Considering the stated measurement uncertainty of ±1 mL, the final volumes are indistinguishable.
Therefore, the results do not support the original hypothesis.
Step 2: Examine the reaction rate.
Reaction rate describes how quickly a reaction occurs.
In this investigation, the volume of hydrogen produced over a fixed time provides evidence of the rate of gas production.
Consider the volume produced during the first 15 seconds.
For 0.50 mol/L:
\[ \text{Mean rate}=\frac{8}{15} =0.53\text{ mL/s} \]
For 1.00 mol/L:
\[ \text{Mean rate}=\frac{16}{15} =1.07\text{ mL/s} \]
For 1.50 mol/L:
\[ \text{Mean rate}=\frac{22}{15} =1.47\text{ mL/s} \]
These are average gas production rates over the first 15 seconds.
The results show a clear increase in early hydrogen production rate as acid concentration increases.
The reaction completion times also decrease:
- 0.50 mol/L: 94 seconds.
- 1.00 mol/L: 46 seconds.
- 1.50 mol/L: 31 seconds.
Therefore, the data support the alternative hypothesis that increasing hydrochloric acid concentration increases the reaction rate under the conditions tested.
Step 3: Explain why the final gas volumes are equal.
This is the important chemical distinction.
The balanced chemical equation is:
\[ \ce{Mg + 2HCl -> MgCl2 + H2} \]
One mole of magnesium produces one mole of hydrogen gas.
The student uses the same mass of magnesium in every experiment, and hydrochloric acid is in excess.
This means magnesium is the limiting reagent.
The limiting reagent is the reactant that is completely consumed first and determines the maximum amount of product that can form.
Because the amount of magnesium is fixed, the maximum amount of hydrogen produced is also fixed.
Increasing the hydrochloric acid concentration does not increase the number of moles of magnesium available to react.
Instead, it increases the number of acid particles per unit volume. This increases the frequency of successful collisions at the magnesium surface, increasing the reaction rate.
The reaction therefore reaches the same final amount of hydrogen more quickly.
This explains why the 1.50 mol/L reaction finishes in 31 seconds, while the 0.50 mol/L reaction takes 94 seconds, even though both produce approximately 40 mL of hydrogen.
Step 4: Identify the mistake in the two arguments.
The first student confuses reaction rate with total yield.
A faster reaction does not necessarily produce a greater total amount of product.
The second student makes the opposite mistake.
Equal final gas volumes do not demonstrate equal reaction rates. The volume-time measurements clearly show that hydrogen is produced at different rates.
A single measurement taken after 60 seconds would also be misleading because the two more concentrated solutions have already completed their reactions by that point.
Step 5: Write an appropriate conclusion.
The original hypothesis must remain unchanged.
An appropriate conclusion would be:
“The results do not support the hypothesis that increasing hydrochloric acid concentration increases the total volume of hydrogen produced from a fixed mass of magnesium. All three concentrations produced approximately 40 mL of hydrogen. However, the results support an increase in reaction rate with increasing acid concentration, as demonstrated by the greater hydrogen volumes produced during the first 15 seconds and the shorter reaction completion times. This occurs because magnesium is the limiting reagent, so the final amount of hydrogen is determined by the fixed amount of magnesium rather than the concentration of excess hydrochloric acid.”
The student can propose the alternative rate hypothesis for a future investigation, but must not pretend that it was the original hypothesis.
This is what makes the result scientifically useful: the investigation has revealed that changing concentration affects how quickly the reaction occurs, but not the final amount of product when the same limiting reagent is completely consumed.
09The next step: use your evaluation to improve the investigation
An unsupported hypothesis gives you a reason to reconsider your scientific model or experimental design.
The next investigation should target the specific uncertainty you identified.
If your measurements were too imprecise, improve the measuring equipment. If two variables changed together, isolate them. If your data suggested a different relationship, design a new investigation that can distinguish between the competing explanations.
The important sequence is simple: record what happened, interpret what the evidence supports, identify what remains uncertain, and use that uncertainty to plan a better investigation.
That is the difference between a report that merely describes an experiment and one that demonstrates genuine scientific reasoning.