Statistical Analysis Fundamentals for Psychology
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1. Type I and Type II Errors
Type I Error (α): Occurs when a researcher rejects a true null hypothesis (a "false positive").
Type II Error (β): Occurs when a researcher fails to reject a false null hypothesis (a "false negative").
The goal of statistical testing is to minimize both errors simultaneously.
2. Parametric vs. Non-Parametric Statistics
Parametric Tests: These assume data is normally distributed and use interval/ratio scales (e.g., t-test, ANOVA).
Non-Parametric Tests: These are "distribution-free" tests used for nominal/ordinal data or small samples (e.g., Chi-square, Mann-Whitney U).
Parametric tests are generally more powerful if their assumptions are met.
3. Null Hypothesis (H₀) vs. Alternative Hypothesis (H₁)
Null Hypothesis (H₀)
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