Chapter 13 Study Guide: One Sample t-Test and Correlation Coefficient

These flashcards cover key concepts from the lecture notes on one sample t-test, correlation coefficient, and ANOVA, summarizing important definitions, formulas, and assumptions related to these statistical tests.

What is the purpose of the one sample t-test?

To test a one-sample experiment when the standard deviation of the raw score population is not known.

What does a t-distribution depend on?

The degrees of freedom (df) of the samples used to create it.

What is the difference between point estimation and interval estimation?

Point estimation assumes 'mu' is at a specific value, while interval estimation assumes 'mu' lies within a specific interval.

What symbol is used for the Pearson correlation coefficient in the population?

Rho (ρ).

When is it appropriate to compute the linear regression equation?

Only when a correlation coefficient is significant.

What factors increase the power of experiments?

Creating large differences in scores, minimizing variability within conditions, and increasing sample size (N).

What is the difference between independent and related samples t-tests?

Independent samples t-test is used when groups are not related or matched, while related samples t-test is used for the same participants measured twice or matched pairs.

What are the assumptions for conducting an independent samples t-test?

Interval or ratio data, normally distributed populations, and homogeneity of variance.

What is the formula for the independent samples t-test?

t{obt} = (X1 - X2) / sqrt(s{pooled}^2 (1/n1 + 1/n2))

What is Cohen’s d used for?

To measure effect size, indicating how large the effect is in independent samples.

What does a significant F in ANOVA indicate?

It indicates that at least one mean is different among the groups.

What does η² (eta squared) represent in ANOVA?

The proportion of variance explained by the independent variable.

What is the formula for the F-ratio in ANOVA?

F = MS{bn} / MS{wn} (Mean square between / Mean square within).