Grade: Grade 11 Subject: Science Unit: ACT Science Reasoning SAT: ProblemSolving+DataAnalysis ACT: Science

Research Summaries

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ACT Science: Research Summaries

Research Summaries passages describe one or more related experiments. These passages test your understanding of experimental design, the scientific method, and your ability to analyze results. About 45% of ACT Science questions are Research Summaries.

Structure of Research Summary Passages

These passages typically include:

  • Background information introducing the topic
  • Description of 2-3 experiments (often labeled Experiment 1, 2, 3)
  • Tables and/or graphs showing results
  • Sometimes a diagram of the experimental setup

Understanding Experimental Design

Term Definition Example
Independent Variable What the researcher deliberately changes Temperature settings in a heat experiment
Dependent Variable What the researcher measures (the result) Reaction rate, plant height, bacterial count
Controlled Variables (Constants) Factors kept the same across all trials Same amount of water, same light exposure
Control Group The baseline group with no treatment Plants with no fertilizer, subjects with placebo
Experimental Group The group receiving the treatment Plants with fertilizer, subjects with medication

The Scientific Method

  1. Observation: Notice a phenomenon
  2. Question: Ask why or how
  3. Hypothesis: Propose a testable explanation
  4. Experiment: Design a procedure to test the hypothesis
  5. Data Collection: Record observations and measurements
  6. Analysis: Look for patterns and draw conclusions
  7. Conclusion: Determine if results support or refute the hypothesis

Common Question Types

Question Type What It Asks Strategy
Experimental Design "What was the purpose of Experiment 2?" or "Why did researchers use a control group?" Re-read the procedure; identify what's being tested
Compare Experiments "How did Experiment 1 differ from Experiment 2?" Look for what changed between experiments
Results Interpretation "According to the results, which trial had the highest yield?" Go to the data table/graph; find specific values
Hypothesis Testing "Do the results support the hypothesis?" Compare predictions to actual results
New Experiment "What additional experiment could test...?" Think about what variable would need to change
Limitations/Improvements "What would improve this experiment?" Look for missing controls, small sample sizes, or confounding variables

Reading Strategy for Research Summaries

  1. Skim the introduction: Get the general topic (don't memorize details)
  2. For each experiment, identify:
    • What was changed (independent variable)?
    • What was measured (dependent variable)?
    • What was kept the same (controls)?
  3. Glance at data: Note general trends, don't analyze deeply yet
  4. Go to questions: Return to specific passages/data as needed

Understanding Experimental Validity

Concept Meaning Example of a Problem
Confounding Variable An uncontrolled factor that could affect results Testing plant growth with different fertilizers, but some plants get more sunlight
Sample Size Number of trials or subjects Testing a drug on only 3 people may not be representative
Replication Repeating the experiment to verify results Running each trial only once doesn't account for random variation
Bias Systematic error that skews results Researcher knows which subjects got the treatment, affecting observations

Key Terms to Know

  • Hypothesis: A testable prediction
  • Procedure: Step-by-step method
  • Data: Observations and measurements collected
  • Conclusion: What the results mean
  • Correlation: When two variables change together (doesn't prove causation)
  • Causation: When one variable directly causes change in another

💡 Examples

Practice analyzing research summary passages.

Example 1: Identifying Variables

Scenario: Researchers tested how water temperature affects the rate at which sugar dissolves. They heated water to 20°C, 40°C, 60°C, and 80°C, added 10g of sugar to each, stirred at the same rate, and timed how long it took to fully dissolve.

Question: What are the independent and dependent variables?

Solution

Independent variable: Water temperature (what researchers changed: 20°C, 40°C, 60°C, 80°C)

Dependent variable: Dissolution time (what was measured)

Controlled variables: Amount of sugar (10g), stirring rate, type of sugar

Example 2: Experimental Purpose

Scenario:

Experiment 1: Researchers grew plants in soil with different pH levels (5, 6, 7, 8) and measured growth after 30 days.

Experiment 2: Using the optimal pH from Experiment 1, researchers then tested different fertilizer concentrations (0%, 5%, 10%, 15%).

Question: Why did researchers conduct Experiment 1 before Experiment 2?

Solution

Answer: Experiment 1 established the optimal soil pH for plant growth. This controlled for pH in Experiment 2, allowing researchers to isolate the effect of fertilizer concentration. If they tested fertilizer at various pH levels simultaneously, they couldn't determine which factor affected growth.

Example 3: Interpreting Results

Scenario: A table shows bacterial colony counts after exposure to different antibiotic concentrations:

  • 0 mg/L: 500 colonies
  • 5 mg/L: 320 colonies
  • 10 mg/L: 150 colonies
  • 15 mg/L: 45 colonies
  • 20 mg/L: 48 colonies

Question: What do these results suggest about antibiotic effectiveness?

Solution

Answer: The antibiotic is effective at reducing bacterial growth up to about 15 mg/L. Colony counts decrease as concentration increases from 0 to 15 mg/L. However, at 20 mg/L, colony count doesn't decrease much further (45 to 48), suggesting that 15-20 mg/L may be the maximum effective concentration—higher doses don't significantly improve results.

Example 4: Comparing Experiments

Scenario:

Experiment 1: Seeds germinated in light at 25°C.

Experiment 2: Seeds germinated in darkness at 25°C.

Experiment 3: Seeds germinated in light at 15°C.

Question: Comparing Experiments 1 and 2 tests the effect of what variable?

Solution

Answer: Light exposure. Both experiments were conducted at 25°C (temperature controlled), but Experiment 1 had light while Experiment 2 was in darkness. This comparison isolates the effect of light on seed germination.

Note: Comparing Experiments 1 and 3 would test the effect of temperature (both in light, different temperatures).

Example 5: Evaluating Experimental Design

Scenario: A student wanted to test if music helps plants grow. She played classical music to one plant and kept another in silence. After 4 weeks, the plant with music was taller.

Question: What is a major limitation of this experiment?

Solution

Major limitations include:

  • Sample size too small: Only one plant per group; results could be due to natural variation between individual plants
  • Possible confounding variables: Were both plants in the same location? Same amount of water? Same pot size?
  • No replication: The experiment was done only once
  • Only one type of music tested: Can't generalize to "music" in general

A better design would use multiple plants per group, ensure identical conditions, and repeat the experiment.

✏️ Practice

Answer these research summary questions.

1. Researchers tested enzyme activity at temperatures of 20°C, 30°C, 40°C, 50°C, and 60°C. What is the independent variable?

2. In an experiment testing fertilizer effects on plant growth, why would researchers include plants that received no fertilizer?

3. Experiment 1 tested drug effectiveness in mice. Experiment 2 tested the same drug in humans. Why might results differ between experiments?

4. A study found that students who eat breakfast score higher on tests. Does this prove that eating breakfast causes better test performance?

5. Researchers measured plant growth under red, blue, green, and white light. All plants received the same amount of water and were at the same temperature. What are the controlled variables?

6. An experiment tested 5 different soil types on tomato plant yield. Each soil type was tested with 3 plants. How many total plants were used?

7. Data shows that as altitude increases, air pressure decreases. Based on this pattern, what would you predict about air pressure at an altitude higher than any measured in the experiment?

8. A researcher found no difference in test scores between students who studied with music vs. silence. She concludes "music has no effect on studying." Is this conclusion valid?

9. In testing a new medication, why do researchers use a placebo (fake pill) for the control group rather than giving them nothing?

10. An experiment found that caffeine improved reaction time. What additional experiment could determine if the improvement is dose-dependent?

Answer Key
  1. Temperature — It's what the researchers deliberately varied (20°C to 60°C).
  2. To serve as a control/baseline — Without unfertilized plants, you can't know if observed growth is due to fertilizer or would have happened anyway.
  3. Mice and humans have different physiologies, metabolism, body size, immune systems, etc. Drugs may work differently in different species.
  4. No — This is correlation, not causation. Students who eat breakfast might also get more sleep, have more stable home environments, or have other factors affecting performance.
  5. Water amount and temperature — These were kept constant across all conditions to isolate the effect of light color.
  6. 15 plants — 5 soil types × 3 plants each = 15 total plants.
  7. Air pressure would continue to decrease — Extrapolating the observed inverse relationship between altitude and pressure.
  8. Partially valid, but limited — The study found no effect in this specific situation, but couldn't test all types of music, all subjects, or all study tasks. The conclusion should be more limited: "In this experiment, music showed no significant effect."
  9. To control for the placebo effect — People often feel better just because they believe they received treatment. Using a placebo ensures any improvement in the treatment group is due to the actual drug, not psychological effects.
  10. Test multiple caffeine doses — Compare reaction times at 0 mg (control), 50 mg, 100 mg, 200 mg, etc. If improvement increases with dose (up to a point), it's dose-dependent.

✅ Check Your Understanding

1. Why is it important to have a control group in an experiment?

Show Answer

A control group provides a baseline for comparison. Without it, you can't know if changes in the experimental group are due to your treatment or would have happened anyway. For example, if plants with fertilizer grew 10 cm, that seems meaningless unless you know plants without fertilizer grew 8 cm (showing fertilizer added 2 cm) or grew 10 cm (showing fertilizer had no effect).

2. What's the difference between correlation and causation?

Show Answer

Correlation means two variables change together (when one increases, the other increases or decreases). Causation means one variable directly causes the change in another. Correlation does NOT prove causation. Ice cream sales and drowning deaths are correlated (both increase in summer), but ice cream doesn't cause drowning—hot weather (a confounding variable) affects both.

3. Why do experiments often test multiple trials or use multiple subjects per group?

Show Answer

Multiple trials/subjects increase reliability by accounting for natural variation. A single plant might grow exceptionally well or poorly due to individual differences unrelated to the treatment. With multiple subjects, random variation averages out, and you can be more confident that differences between groups are due to the treatment, not chance. This also allows for statistical analysis.

4. An experiment's results don't support the original hypothesis. Is the experiment a failure?

Show Answer

No! A well-designed experiment that disproves a hypothesis is still valuable science. It tells us what doesn't work, which narrows down possibilities and guides future research. Many important discoveries came from unexpected results. The key is that the experiment was properly designed and executed—not that it confirmed what we expected.

🚀 Next Steps

  • Review any concepts that felt challenging
  • Move on to the next lesson when ready
  • Return to practice problems periodically for review