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Methodical & Stats Analysis Flashcards

7 cards from real BAPSY practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.

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  1. Which of the following is a non-parametric alternative to the independent-samples t-test?

    Answer: Mann-Whitney U test

    The Mann-Whitney U test is the non-parametric equivalent of the independent-samples t-test, comparing two unrelated groups using ranks.

  2. Regression toward the mean is most likely to threaten internal validity in which design?

    Answer: Pre-test/post-test designs without a control group

    When participants are selected based on extreme scores, scores naturally move toward the mean on retest, threatening internal validity in pre/post designs without controls.

  3. Which validity type is threatened when a measure does not adequately sample all facets of a construct?

    Answer: Content validity

    Content validity concerns whether a measure comprehensively covers the full domain of the construct being assessed.

  4. In meta-analysis, publication bias refers to:

    Answer: The tendency for significant results to be published more than null results

    Publication bias occurs because journals disproportionately accept significant findings, inflating meta-analytic effect size estimates.

  5. A researcher uses stratified random sampling. The primary advantage over simple random sampling is:

    Answer: It guarantees representation of key subgroups

    Stratified sampling divides the population into subgroups and samples from each, ensuring proportional representation of important strata.

  6. The standard error of the mean (SEM) decreases when:

    Answer: Sample size increases

    SEM = σ/√n, so increasing sample size reduces the standard error, making sample means more precise estimates of the population mean.

  7. When a researcher randomly assigns participants to conditions, the primary purpose is to:

    Answer: Equate groups on known and unknown confounds at baseline

    Random assignment distributes both known and unknown participant characteristics evenly across conditions, controlling for confounds.