Week 9-13 Flashcards
(160 cards)
What non-parametric test do you use for independent samples?
Mann-Whitney U test =Z
What non-parametric test do you use for independent samples?
Mann-Whitney U test
What non-parametric test do you use for paired samples?
Wilcoxon-Rank Sum
What non-parametric test do you use for a one way ANOVA?
Kruskal-Wallis test
What non-parametric test do you use for a repeated measures ANOVA?
Firedman’s test
What non-parametric test do you use for a mixed factorial ANOVA?
There is currently no know test for this design
What non-parametric test do you use for paired samples?
Wilcoxon-Rank Sum =Z
What non-parametric test do you use for a one way ANOVA?
Kruskal-Wallis test =H
What non-parametric test do you use for a repeated measures ANOVA?
Firedman’s test X2f
What non-parametric test do you use for a mixed factorial ANOVA?
There is currently no know test for this design
What two reasons would make you use a non-parametric test?
When you have ordinal or nominal data,
When your data is not normally distributed
What do you need to calculate effect size?
desired alpha, desired effect size, level of Power
Can the statistics of effect size and power be applied to all statistical tests that involve significance testing processes?
Yes
Is there ALWAYS a chance of us as researchers making an error?
yes always.
1 - a is the ____ ?
probability of you being right
Beta is the ___ ?
probability that you incorrectly retained the null hypothesis
Historically, what type of error was ignored ?
Type II error
The chance of not finding a difference even though one is really there is? (you said there was no difference, but there there really was)
A type II error
What is beta normally estimated to be?
.2 (20%)
What is power?
The probability that a statistical test will correctly reject the false null hypothesis (1 - b)
Do we want power to be big or small?
big as possible
Are 1 - a and 1 - b related?
Yes
What affects power? (4 things)
a -alpha, The alternative hypothesis, Sample size, population variance,
What two ways can you increase your power in a study?
A larger sample size, and less variance between the sample and the population