# Interpreting Biology Research Results
After understanding an experiment's design, you need to interpret the results: What do the data show? What conclusions are valid? What are the limitations? These are the most common question types in ACT Science Research Summaries.
1. Drawing Conclusions from Data
Steps
- State the trend: Describe what the data show (e.g., "As fertiliser concentration increased, plant height increased")
- Relate to hypothesis: Does the data support or refute the original hypothesis?
- Consider significance: Are the differences meaningful or could they be due to chance?
- Stay within the data: Don't extrapolate beyond the conditions tested
Common Conclusion Patterns
- "The results support the hypothesis that…"
- "There is a positive/negative correlation between X and Y"
- "The results suggest that… however, further testing is needed to confirm causation"
2. Evaluating Claims
ACT questions may ask whether a particular claim is supported by the data:
Supported Claims
- Directly backed by data trends
- Within the range of conditions tested
- Consistent across multiple experiments/trials
Unsupported Claims
- Go beyond the data (extrapolation)
- Confuse correlation with causation
- Ignore controlled variables or alternative explanations
- Cherry-pick data points
3. Worked Example
Experiment: Researchers tested the effect of light duration on flowering in two plant species. Results:
| Light Duration (hours/day) | Species A (% flowering) | Species B (% flowering) |
|---|---|---|
| 8 | 90 | 5 |
| 12 | 60 | 40 |
| 16 | 10 | 95 |
Q1: Which claim is supported by the data?
- (A) Species A is a long-day plant
- (B) Species A is a short-day plant
- (C) Both species require the same light conditions
- (D) Neither species is affected by light duration
A1: (B) Species A flowers best with short days (8 hours: 90%), while Species B flowers best with long days (16 hours: 95%). Species A is a short-day plant.
Q2: A student claims: "All plants flower better in short days." Is this supported?
A2: No. The data show that Species B flowers better in long days (95% at 16h vs 5% at 8h). The claim over-generalises from Species A's results and ignores Species B.
4. Identifying Limitations
Common Limitations in Biology Experiments
- Small sample size: May not represent the whole population
- Single species tested: Can't generalise to all organisms
- Short duration: May miss long-term effects
- Lab conditions: May not reflect natural environments
- Confounding variables: Uncontrolled factors that could affect results
5. Practice Questions
Q1. An experiment shows antibiotic X kills 99% of bacteria in a petri dish. Can you conclude it will cure bacterial infections in humans?
A1. No. In vitro (lab) results don't always translate to in vivo (in the body). Factors like drug absorption, immune response, toxicity, and the complexity of the human body are not tested in a petri dish.
Q2. Two groups of mice were fed different diets. Group A (high-protein) gained more muscle mass. The researcher concludes protein causes muscle growth. What's missing?
A2. The researcher should control for total calorie intake, exercise levels, and mouse genetics. Without controlling these variables, the difference might not be solely due to protein content. Also, with only two groups, other dietary differences (fat, carbohydrates) could be confounding factors.
Want to check your answers and get step-by-step solutions?
Summary
- Draw conclusions that are directly supported by the data — no more, no less
- Distinguish between correlation and causation
- Identify limitations: sample size, duration, generalisability, confounding variables
- On the ACT, the correct answer is always the one most directly supported by the passage data