statistics (321)

statistical inference (324)

mean (324)

variability (324)

null hypothesis (325)

statistical significance (326)

alternative hypothesis (H₁) (326)

frequency distribution of scores (328-329)

effect of variability on rejecting the null hypothesis (330)

directional hypothesis (330)

normal curve (331)

significance level (333)

Type 1 error (336-339)

Type 2 error (336-339)

importance of variability (339-340)

critical regions (340)

two-tailed test (342)

nondirectional hypothesis (342)

one-tailed test (343)

inferential (test) statistics (344-345)

raw data (346)

summary data (346)

descriptive statistics (346)

measures of central tendency (346)

median (346)

mode (346)

variability (349)

range (349)

variance (351)

standard deviation (353)
 

levels of measurement (361)

ratio scale (361)

interval scale (361)

ordinal scale (361)

nominal scale (361)

chi square test (363-364)

obtained frequencies for chi square (365)

expected frequencies for chi square (365)

critical value for chi square (366)

degrees of freedom for chi square (366)

Cramer's coefficient Phi (368)

t test (369)

effects of sample size (369-371)

robustness of the t test (370)

t test for independent groups (372)

degrees of freedom for t test for independent groups (373)

effect size (r) (375)

confidence intervals (376-377)

t test for matched groups (378)

degrees of freedom for t test for matched groups (380)

analysis of variance (ANOVA) (388)

within-groups variability (389)

between-groups variability (389)

error (390)

F ratio (391)

sources of variability in an experiment (391)

one-way between-subjects analysis of variance (392)

sum of squares (SS) (394)

mean square (394)

mean square within groups (MS_W) (396)

grand mean (396)

sum of squares between groups (SS_B) (396)

mean square between groups (MS_B) (396)

critical F value (399)

effect of degrees of freedom on the F distribution (400)

analysis of variance summary table (401)

post hoc tests (402)

a priori comparisons (402)

eta² (403)

analysis of covariance (ANCOVA) (403)

one-way repeated measures ANOVA (405)

main effects (406)

two-way analysis of variance (408)

summary table (414)

evaluating F ratios (415)

calculating effects sizes (416-417)

repeated measures designs (419)

mixed factorial designs (419)

1. When should an a priori test be used? 2. What does it add to what an F test tells us?
3. What advantages does an a priori test have over a post hoc test? 4. When would you
use a post hoc test instead of an a priori test?

internal validity (430)

manipulation check (431)

Orne's pact of ignorance (433)

statistical conclusion validity (434)

effect size (435)

generalizing from the results (435-436)

generalizing across subjects (436-437)

generalizing from procedures to concepts (437-439)

aggregation (440)

aggregation over subjects (440)

aggregation over stimuli or situations (440)

aggregation over trials or occasions (440)

aggregation over measures (441)

multivariate designs (441)

multivariate analysis of variance (MANOVA) (442)

reactivity (442)

unobtrusive measures (443)

field experiment (444)

naturalistic observation (445)

faulty procedures (446-448)

faulty hypothesis (448)

Explain the importance of effect size when interpreting experimental results.