PSY 3-1 Flashcards
(23 cards)
What is the “World of Me: A Land of Zero Variability”?
A hypothetical world where everyone behaves exactly the same, making statistical comparisons trivial.
What is the null hypothesis (H₀) in an experiment testing a drug’s effect?
H₀: The drug has no effect (μE = μC).
What is the alternative (research) hypothesis (H₁)?
H₁: The drug does have an effect (μE ≠ μC).
How do you decide whether to reject the null hypothesis?
If the p-value ≤ 0.05, reject H₀; otherwise, do not reject H₀.
What does a p-value represent?
The probability of obtaining a statistic as extreme as (or more extreme than) the observed value if H₀ is true.
What statistical test compares two sample means?
t-test
What are Type I and Type II errors?
Type I error: Reject H₀ when it’s true (false positive).
Type II error: Fail to reject H₀ when it’s false (miss).
What symbol represents the probability of a Type I error?
Alpha (α).
What is “power” in hypothesis testing?
1 – β (the probability of correctly rejecting a false null hypothesis).
How are α and β related?
They are inversely related; decreasing one increases the other.
When is a Type II error considered worse than a Type I error?
When failing to detect a real effect (e.g., missing a better teaching method or a dangerous student).
What are the default common values for α?
0.05 or 0.01.
What statistical tests are mentioned for analyzing data?
t-tests (independent, related-samples)
ANOVA (independent-groups, repeated-measures)
Chi-square (nominal data)
Correlation (continuous variables)
What is the formula for degrees of freedom (df) for a paired-samples t-test?
df = N – 1
What is the formula for degrees of freedom for an independent samples t-test?
df = N₁ + N₂ – 2
How is the effect size (r) related to t?
Effect size increases as t increases.
What are the benchmarks for interpreting effect size r?
r = .15 → small
r = .30 → medium
r = .40 → large
Effect size
tells you how big the difference or relationship is, not just whether it exists.
What is statistical power?
The probability of correctly detecting an effect (Power = 1 - β).
Power Analysis:
Given an effect size (for a particular IV in a particular situation) and the level of
significance, determine N needed to detect effect
To increase power,
increase the sample size
To detect smaller effects,
increase sample size
What are two reasons non-significant results might occur even when H₀ is false?
α is set very low.
Sample size is too small for the effect size.
