ACT Prep: Research Summaries (Experimental Design, Variables, Hypothesis, Results)

ACT – Research Summaries (Experimental Design, Variables, Hypothesis, Results)

ACT Research Summaries Study Guide

(Experimental Design, Variables, Hypothesis, Results)

What This Is

Research Summaries passages on the ACT Science section describe real experiments, including their design, variables, hypotheses, and results. You’ll analyze data tables, graphs, and text to answer questions about experimental setup, controls, and conclusions. Why it matters: These passages make up ~30-40% of ACT Science questions, and mastering them can boost your score by 2-4 points. Example test question: "Which variable was intentionally changed by the researchers to test its effect on plant growth?"

Key Terms & Rules

• Independent Variable (IV): The variable intentionally changed by the researcher to observe its effect (e.g., amount of fertilizer given to plants).
• Dependent Variable (DV): The measured outcome that responds to changes in the IV (e.g., plant height after 4 weeks).
• Controlled Variables (Constants): Factors kept the same across all trials to ensure a fair test (e.g., same type of soil, sunlight, water).
• Control Group: A baseline trial without the IV applied (e.g., plants given no fertilizer). Used for comparison.
• Experimental Group: Trials where the IV is applied (e.g., plants given 5g, 10g, or 15g of fertilizer).
• Hypothesis: A testable prediction about the relationship between IV and DV (e.g., "Increasing fertilizer will increase plant growth").
• Confounding Variable: An uncontrolled factor that could skew results (e.g., some plants accidentally get more sunlight).
• Trend: The pattern in data (e.g., "As fertilizer increases, plant height increases").
• Outlier: A data point that doesn’t fit the trend (e.g., one plant dies despite high fertilizer).
• Sample Size: Number of trials or subjects (e.g., 20 plants per group). Larger samples = more reliable results.
• Replication: Repeating an experiment to confirm results (e.g., running the plant study 3 times).
• Correlation vs. Causation: Correlation = variables change together; causation = one variable directly causes the other. ACT loves testing this distinction!

Step-by-Step / Process Flow

How to tackle a Research Summaries passage:
1. Skim the intro/hypothesis first → Identify the IV, DV, and purpose of the experiment.
2. Scan the methods → Note the control group, constants, and how data was collected.
3. Analyze data tables/graphs → Look for trends, outliers, or unexpected results. Circle key numbers! 4. Read the question → Underline what it’s asking (e.g., "Which group was the control?").
5. Eliminate wrong answers → Cross out choices that contradict the data or passage.
6. Check units/labels → ACT often swaps units (e.g., cm vs. mm) or mislabels axes.

Common Mistakes

• Mistake: Confusing IV and DV.
  Correction: Ask: "What did the researcher change?" (IV) vs. "What did they measure?" (DV). Why? ACT traps you by swapping them in answer choices.

• Mistake: Ignoring the control group.
  Correction: Always identify the control group first—it’s the baseline for comparison. Why? Questions often ask, "Compared to the control, what happened in the experimental group?"

• Mistake: Assuming correlation = causation.
  Correction: Just because two variables trend together doesn’t mean one causes the other. Why? ACT includes distractors like "Fertilizer caused taller plants" when the data only shows a correlation.

• Mistake: Overlooking constants.
  Correction: If a question asks, "Why was soil type kept the same?" the answer is always about controlling variables. Why? ACT tests if you know constants ensure a fair test.

• Mistake: Misinterpreting outliers.
  Correction: Outliers don’t fit the trend—don’t ignore them! Why? ACT may ask, "Which trial is an outlier?" or "Why might this result differ?"

Exam Insights

• Most-tested concept: Identifying IV/DV/control group. Expect 2-3 questions per passage on this.
• Tricky distractor: ACT often includes confounding variables as answer choices (e.g., "Sunlight affected plant growth" when sunlight wasn’t mentioned).
• Data vs. text: Always prioritize data over the passage’s hypothesis. ACT may say "The hypothesis was supported" but show data that contradicts it.
• Graph traps: Watch for reversed axes (e.g., time on the y-axis) or non-linear scales (e.g., logarithmic).

Quick Check Questions

1. A study tests how caffeine affects reaction time. Participants drink 0mg, 50mg, or 100mg of caffeine, then complete a reaction-time test. What is the independent variable?
2. A) Reaction time
3. B) Amount of caffeine
4. C) Number of participants

5. D) Time of day
   Answer: B) Amount of caffeine (The IV is what the researcher changes.)

6. In the same study, the researchers keep the room temperature and lighting the same for all trials. Why?

7. A) To make the experiment easier
8. B) To control variables that could affect reaction time
9. C) To increase the sample size

10. D) To test multiple hypotheses
    Answer: B) To control variables (Constants ensure a fair test.)

11. A graph shows that as study time increases, test scores increase. Which statement is not supported by the data?

12. A) Study time and test scores are correlated.
13. B) More study time causes higher test scores.
14. C) Students who studied longer scored higher.
    Answer: B) More study time causes higher test scores (Correlation ≠ causation! The data doesn’t prove cause.)

Last-Minute Cram Sheet

1. IV = what’s changed; DV = what’s measured.
2. Control group = no IV; experimental group = IV applied.
3. Constants = factors kept the same (e.g., temperature, time).
4. ⚠️ ACT swaps IV/DV in answer choices—double-check!
5. Always identify the control group first.
6. Outliers = data points that don’t fit the trend.
7. Correlation ≠ causation—don’t assume cause!
8. ⚠️ Prioritize data over the passage’s hypothesis.
9. Check graph axes and units—ACT loves to trick you here.
10. Sample size matters: larger = more reliable results.