GED Prep: Scientific Reasoning (Hypothesis, Experimental Design, Data Interpretation, Bias)

GED – Scientific Reasoning (Hypothesis, Experimental Design, Data Interpretation, Bias)

GED Scientific Reasoning Study Guide

Topic: Hypothesis, Experimental Design, Data Interpretation, Bias

What This Is

Scientific Reasoning on the GED tests your ability to evaluate experiments, interpret data, and identify bias—skills critical for real-world decision-making. You’ll analyze scenarios like a scientist testing a new fertilizer’s effect on plant growth or a study on sleep and memory. A typical question might ask: "Which hypothesis is testable and specific?" or "What is the control group in this experiment?" Mastering this topic ensures you can separate fact from opinion, a key skill for college and careers.

Key Terms & Rules

• Hypothesis: A testable, specific prediction (e.g., "Increasing study time by 30 minutes daily will improve test scores by 10%."). Avoid vague statements ("Studying helps").
• Independent Variable (IV): The factor changed by the experimenter (e.g., amount of fertilizer).
• Dependent Variable (DV): The measured outcome (e.g., plant height).
• Control Group: The baseline group not exposed to the IV (e.g., plants with no fertilizer). Used for comparison.
• Experimental Group: The group exposed to the IV (e.g., plants with fertilizer).
• Controlled Variables (Constants): Factors kept the same for all groups (e.g., sunlight, water, pot size).
• Sample Size: Number of participants/items tested. Larger samples = more reliable results.
• Bias: Systematic error that skews results (e.g., only testing the fertilizer on one plant species).
• Selection Bias: Non-random sampling (e.g., only surveying coffee drinkers about caffeine’s effects).
• Confirmation Bias: Favoring data that supports pre-existing beliefs.
• Correlation vs. Causation: Correlation = two variables change together (e.g., ice cream sales and drowning rates rise in summer). Causation = one variable causes the other (requires controlled experiments).
• Placebo Effect: Participants’ expectations influence results (e.g., feeling better after a sugar pill). Controlled with a placebo group.
• Double-Blind Study: Neither participants nor researchers know who’s in the control/experimental group (reduces bias).
• Data Interpretation: Look for trends (increasing/decreasing), outliers, and statistical significance (e.g., "p < 0.05" means results are likely not due to chance).

Step-by-Step / Process Flow

1. Identify the Hypothesis

• Action: Read the scenario and underline the hypothesis. Ask: "Is it testable and specific?"
• Example: "Does drinking 2 cups of coffee daily improve focus?" (Good) vs. "Coffee is good for you." (Bad—too vague).

2. Determine Variables and Groups

• Action: Label the IV, DV, control group, and experimental group.
• Example: "A study tests if a new drug lowers blood pressure."
• IV = drug dosage (0 mg vs. 50 mg).
• DV = blood pressure.
• Control group = 0 mg (placebo).
• Experimental group = 50 mg.

3. Check for Bias or Flaws

• Action: Ask:
• Is the sample size large enough?
• Are variables controlled?
• Is there a placebo/control group?
• Could bias (e.g., selection, confirmation) affect results?

4. Interpret Data

• Action: For graphs/tables:
• Identify trends (e.g., "As X increases, Y decreases").
• Note outliers (data points far from the trend).
• Compare groups (e.g., "The experimental group’s blood pressure dropped by 10% vs. 2% in the control group.").

5. Draw Conclusions

• Action: Match the data to the hypothesis. Ask:
• Does the data support or refute the hypothesis?
• Is there a clear cause-effect relationship, or just correlation?
• Are the results statistically significant?

Common Mistakes

Exam Insights

• Most-Tested Concepts:
• Identifying IV/DV and control groups.
• Spotting bias (especially selection and confirmation bias).
• Distinguishing correlation from causation.
• Tricky Distractors:
• Answer choices that describe correlation as causation (e.g., "A study shows people who eat breakfast score higher on tests, so breakfast causes better grades.").
• Hypotheses that are not testable (e.g., "Aliens built the pyramids.").
• Calculator Tip: Use the GED calculator to compare percentages or calculate averages (e.g., "The experimental group’s scores improved by 15% vs. 5% in the control group.").
• Real-World Connection: GED questions often use health, environment, or technology examples (e.g., "Does a new app improve memory?").

Quick Check Questions

1.

A researcher wants to test if a new energy drink improves reaction time. She gives the drink to 10 athletes and measures their reaction time before and after drinking it. What is the biggest flaw in this experiment? A) No control group B) Small sample size C) No placebo group D) All of the above

Answer: D) All of the above. Explanation: The study lacks a control group (no comparison), has a small sample size (10 people), and no placebo group (could cause bias).*

2.

Which hypothesis is testable and specific? A) "Exercise is good for health." B) "Running 30 minutes daily for 4 weeks will lower resting heart rate by 5%." C) "People who exercise are happier." D) "Exercise might improve mood."

Answer: B) "Running 30 minutes daily for 4 weeks will lower resting heart rate by 5%." Explanation: It’s specific, measurable, and time-bound (unlike the vague options).*

3.

Look at the graph below. What conclusion can you draw? (Graph shows: As hours of sleep increase, test scores increase.) A) More sleep causes higher test scores. B) Higher test scores cause more sleep. C) There is a correlation between sleep and test scores, but causation cannot be proven. D) Sleep has no effect on test scores.

Answer: C) There is a correlation between sleep and test scores, but causation cannot be proven. Explanation: The graph shows a relationship, but without a controlled experiment, we can’t prove sleep causes higher scores.*

Last-Minute Cram Sheet

1. Hypothesis: Must be testable and specific (e.g., "X will increase Y by Z%").
2. IV = what you change; DV = what you measure.
3. Control group = no IV; experimental group = IV.
4. Correlation-causation. Don’t assume cause-effect without an experiment.
5. Bias = systematic error. Look for selection bias, placebo effects, or researcher bias.
6. Sample size matters. Small samples (e.g., <30) are less reliable.
7. Double-blind studies reduce bias. Neither participants nor researchers know who’s in which group.
8. Placebo effect: Expectations can influence results (use a placebo group).
9. Controlled variables: Keep everything except the IV the same.
10. Common trap: Answer choices that confuse correlation with causation. Always check the data!