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psych stats final
| Question | Answer |
|---|---|
| inferential statistics | Statistical tests that allow us to make generalizations about a population based on a sample |
| t test | An inferential statistical test that compares one dependent (outcome variable) between two groups |
| t curve | The modified normal curve, created by william gosset and used to determine statistical significance when performing a t test |
| degrees of freedom (df) | How much freedom statisticians have to vary the numbers in their data set and still get the same parameter |
| t statistic | the t value calculated by a t test |
| Cohen's d | The effect size measure used when conducting a t test |
| one-sample t test | a t test that compares one sample of data with a test value (that is, the value believed to be true if the null hypothesis is supported) |
| test value | the value believed to be true if the null hypothesis is supported. This is often, but not always, a mean value |
| between-subject research design | A research design that studies the differences between two different groups of participants, with each group making up one condition of the independent (outcome) variable |
| independent t test | A t test that measures whether two populations (represented by independently selected groups) differ from each other on a quantitative outcome variable |
| paired-sample t test or dependent t test | a t test used when the participants are not selected at random. Instead, the participants are paired based on similarities, or the same research participants are studied at two points in time |
| within-subject research design | A research design that evaluates how one person or thing behaves before and after experimental manipulation |
| Repeated measures research design | a type of within-group research design in which participants serve as both the control group and the experimental group |
| What does the null hypothesis (H₀) state? | There is no effect or no difference in the population. |
| What is an example of a null hypothesis? | H₀: μ = 50 (the population mean equals 50). |
| What does the alternative hypothesis (H₁ or Hₐ) state? | There is an effect or difference. |
| What is a two-tailed alternative hypothesis example? | Hₐ: μ ≠ 50 (the population mean is not equal to 50). |
| What is a right-tailed alternative hypothesis example? | Hₐ: μ > 50 (the population mean is greater than 50). |
| What is a left-tailed alternative hypothesis example? | Hₐ: μ < 50 (the population mean is less than 50). |
| When is a directional test (one-tailed) used? | When the researcher predicts the direction of the effect. |
| What is an example of a directional test? | Energy drink increases heart rate. |
| When is a non-directional test (two-tailed) used? | When the researcher predicts a difference but not the direction. |
| What does a p-value represent? | The probability of observing results as extreme or more extreme than the sample result if the null hypothesis is true. |
| What does a small p-value suggest? | The result is unlikely under the null hypothesis. |
| How do you make a statistical decision using p and α? | Compare the p-value to alpha (α); if p ≤ α, reject the null hypothesis. |
| What is the usual value of alpha (α)? | α = .05. |
| What is a test statistic? | Measures how far the sample result is from the null hypothesis in units of standard error. |
| What is an example of a test statistic? | z score or t statistic. |
| What is effect size? | Measures how large the effect actually is. |
| What is Cohen's d? | A measure of effect size calculated as mean difference divided by standard deviation. |
| Which is affected by sample size: p-value or effect size? | p-value is affected by sample size. |
| Why is it important to report effect size along with p-values? | To provide context on the practical importance of the results. |
| Can small effect sizes be important? | Yes, especially if the effect impacts many people or is rare but significant. |
| What does a very small p-value indicate? | The result is unlikely under the null hypothesis but does not indicate the size or importance of the effect. |
| Why is Cohen's d preferred over raw mean difference? | Cohen's d standardizes the effect, allowing comparison across studies. |
| What does a Cohen's d of 0.2 indicate? | A small effect size. |
| What does a Cohen's d of 0.5 indicate? | A medium effect size. |
| What does a Cohen's d of 0.8 indicate? | A large effect size. |
| Effects of Increasing Sample Size Assume sample mean stays the same. | Type I error rate = No change α (alpha) = No change Type II error rate = Decreases Power = Increases |
| Effects of Increasing Sample Size Assume sample mean stays the same. | β = Decreases p-value = Decreases Cohen’s d = No change Width of CI = Decreases (narrower) |
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