Study guides › P/S › Research Methods & Statistics
Inferential Statistics & Interpreting Results
Inferential statistics let researchers judge whether a pattern in a sample reflects the population. You will learn hypothesis testing, p-values, Type I and II errors, power, confidence intervals, which test fits which design, and how to weigh statistical significance against real-world importance.
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What this chapter covers
- Hypothesis Testing, P-Values & Alpha. Every results passage asks whether a finding is 'significant' and what that word does and does not mean.
- Type I & II Errors and Statistical Power. Two ways to be wrong, and one way to describe how likely you are to be right, appear in nearly every stats passage.
- Confidence Intervals & Standard Error. A confidence interval says more than a p-value: it shows the plausible range of the true value and how precise the estimate is.
- Choosing a Statistical Test. Passages name tests such as t-test or chi-square and expect you to match each one to its data type and design.
- Effect Size, Significance & Interpreting Results. A significant p-value is not the end of the story: the MCAT wants you to judge how big, how meaningful, and how well supported a result is.
Key points to know
- H₀ = no effect; the test asks how surprising the data are under H₀
- p < α means reject H₀ (statistically significant)
- Type I = false positive (rejecting a true H₀), probability α
- Type II = false negative (missing a real effect), probability β
- 95% CI: 95% of such intervals contain the true value
- CI ≈ estimate ± 2 SE; SE = SD/√n
- t-test: two means; paired for repeated or matched measures
- ANOVA: three or more means, F ratio, followed by post hoc tests
Try a practice question
A researcher sets α = 0.05 and obtains p = 0.03. What is the correct decision?
- Reject the null hypothesis; the result is statistically significant
- Fail to reject the null hypothesis
- Accept the alternative hypothesis as proven
- Conclude that the effect is large
Show the answer
A. Because 0.03 is below α, the null is rejected and the result is significant. 'Proven' overstates the evidence, and p says nothing about effect size.
Common questions
What does this chapter cover?
Inferential Statistics & Interpreting Results covers Hypothesis Testing, P-Values & Alpha, Type I & II Errors and Statistical Power, Confidence Intervals & Standard Error, Choosing a Statistical Test, Effect Size, Significance & Interpreting Results.
What should I know first?
H₀ = no effect; the test asks how surprising the data are under H₀ p < α means reject H₀ (statistically significant) Type I = false positive (rejecting a true H₀), probability α
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