Help
Options
Didn't know it?
click below
click below
Knew it?
click below
click below
Don't Know
Remaining cards (0)
Know
retry
shuffle
restart
0:00
Embed Code - If you would like this activity on your web page, copy the script below and paste it into your web page.
Normal Size Small Size show me how
Normal Size Small Size show me how
I/O Psych 542
Exam 2 Part 1
| Question | Answer |
|---|---|
| Assumptions (as in statistical) | a characteristic that we ideally require the population from which we are sampling to have so that we can make accurate inferences |
| Robust | hypothesis tests that produce fairly accurate results even when the data suggest that the population might meet some of the assumptions |
| Parametric Tests | an inferential statistical analysis that is based on a set of assumptions about the population – three main assumptions for parametric testing |
| three main assumptions for parametric testing | *dv assessed w/a scale measure (equal distance between #s) *partic. randomly selected *distr. of pop. of interest must b normal–bc hypothesis tests w/sample means vs. individual scores, if samp. size is 30 (based on central limit theorem)assumption met |
| Non-parametric Tests | Non-parametric Tests – an inferential statistical analysis that is not based on a set of assumptions about the population |
| Critical Values | a test statistic value beyond which we will reject the null hypothesis, often called a cutoff |
| Critical Regions | the area in the tails of the distribution within which we will reject eh null hypothesis if our test statistic falls there |
| p Level or Value | the probability used to determine the critical values, or cutoffs, in hypothesis testing; also called alpha |
| statistically significant | describes a finding for which we have rejected the null hypothesis because the pattern in the data differs from what we would expect by chance |
| One Tailed Test | a hypothesis test in which the research hypothesis is directional, positing either a mean decrease or a mean increase in the dependent variable, but not both, as a result of the independent variable |
| Two-Tailed Test t-Statistic | a hypothesis test in which the research hypothesis does not indicate a direction of mean difference or change in the dependent variable but merely indicates that there will be a mean difference |
| Single Sample t-Test | a hypothesis test in which we compare data from one sample to a population for which we know the mean but not the standard deviation |
| Paired Samples t test | a test used to compare two means for a within-groups design, a situation in which every participant is in both samples, also called a dependent samples t-test. Ex: pre-test/post-test, paired samples (husband/wife, parent/child), repeated measures design |
| Independent-Samples t-Test | a hypothesis test used to compare two means for a between-groups design, a situation in which each participant is assigned to only one condition. Ex: two completely different sets of samples |
| Degrees of Freedom | the number of scores that are free to vary when estimating a population parameter from a sample |
| Pooled Variance | a weighted average of the two estimates of variance – one from each sample – that are calculated when conducting an independent-samples t test |
| ANOVA | a hypothesis test typically used with one or more nominal independent variables (with at least three groups overall) and a scale dependent variable (analysis of variance) |
| F statistic | a ratio of two measures of variance: (a) between-groups variance, which indicates differences among the sample means and (b) within-groups variance, which is essentially an average of the sample variances |
| Between group variance | an estimate of the population variables based on the differences among the means (ex: comparing the pace people talk from Chicago, Shreveport and New York) |
| Within Group Variance | an estimate of the population variance based on the differences within each of the three (or more) sample distributions (ex: comparing the pace people talk in Shreveport – not everyone in the same city talks the same speed) |
| 1 way ANOVA | a hypothesis test that includes one nominal independent variable with more than two levels and a scale dependent variable |
| Within Groups ANOVA | a hypothesis test in which there are more than two samples and each sample is composed of the same participants (aka: repeated measures ANOVA) |
| Between Groups ANOVA | a hypothesis test in which there are more than two samples and each sample is composed of different participants |
| homoscedastic | describes populations that have the same variance; also called homogeneity of variance |
| Heteroscedastic | describes populations that have different variances |
| Post Hoc Tests | a statistical procedure frequently carried out after we reject the null hypothesis in an analysis of variance; it allows us to make multiple comparisons among several means |
| Planned Comparisons | a.Tukey HSD Test –widely used post-hoc test that determines differences between means in terms of standard error; the HSD is compared to a critical value – sometimes called the q test b.Scheffe’s Test – post hoc test –one of the more conservative tests |
| Factor | describes an independent variable in a study with more than one independent variable |
| Cell | a box that depicts one unique combination of levels of the independent variables in a factorial design |
| 2 way ANOVA | a hypothesis test that includes two nominal independent variables, regardless of their numbers of levels, and a scale dependent variable |
| Factoral ANOVA | a statistical analysis used with one scale depdenent variable and at least two nominal independent variables (sometimes called factors); also called a multifactorial ANOVA |
Created by:
cjd021
Popular Psychology sets