The Type 1 error refers to rejecting the null hypothesis when it is true. The Type 2 error is when one rejects the alternative hypothesis even though it is true.
3. Parametric and non-parametric tests
Parametric tests usually deal with samples that have a normal distribution and that are measured with means and standard deviations. T-tests and ANOVA are examples of this comparing one group against another. The Pearson would be used with correlation. Non-parametric tests, however deal with populations where there is no or vague assumptions about the shape or parameters of the population (for instance when one is dealing with a very small sample of offenders that cannot be measured with parametric instruments). Non-parametric tests include Wilcoxon rank-sum test and Kruskal-Wallis test as t-test / ANOVA contrast as well as Spearman's rank correlation instead of Pearson's. (Parametric and Nonparametric: Demystifying the Terms)
4. T-test and ANOVA
The t-test is used for 2 groups, control and experimental. The Anova is used for more than 2 groups. Both are parametric tests. The MANOVA is used for when many different components are expected to be involved in the study and researcher wants to study which one is likely to be most responsible for results. There are three types of t-test:
1. One-sample-t-test - Compares mean of sample with known population mean
2. The independent two-sample t-test is used to determine if the means of two independent samples are equal (for...
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