psych stats

• Repeated Measures Experiments involve comparing the scores of individuals in one condition against their scores in another condition.
• Independent Groups Design involves comparing the scores of one group of people taking one condition against the scores of a different group of people in the other condition.
• Repeated Measures Design, also known as Within-subjects studies or designs, involves comparing the scores of individuals within a group, rather than between two groups.
• Related groups or design involves people undergoing two different treatments being closely matched, so that the two groups are not independent, rather they are related.
• The Sign Test is used to determine if the needles are equivalent.
• The Sign Test involves looking for a Probability value associated with the value of S.
• The Sign Test requires two values: S and N.
• In the Sign Test, the appropriate row is chosen equal to the value of N.
• If the Probability value associated with the value of S is greater than 0.05, the null hypothesis that the needles are equivalent cannot be rejected.
• Cross-over studies or design, mainly used in medical research, involves people crossing over from one group to the other group.
• Correlational Design is when we want to see if people who were high scorers on one test are also high scorers on the second test, without being interested in whether the scores overall have gone up or down.
• Repeated Measures Design has the advantage of not needing many participants and each person acting as their own perfectly matched control group.
• Disadvantages of Repeated Measures Design include practice effects, sensitization, and carry-over effects.
• Statistical Tests for Repeated Measures Designs include the Repeated Measures t-test, the Wilcoxon test, and the Sign Test.
• The Repeated Measures t-test is a parametric test for continuous data and is the most powerful, and most likely to spot significant differences in data.
• The t-test is used to test the statistical significance of a t-score.
• The p-value of a t-score is the probability of getting the score as a result of chance if the null hypothesis is true.
• For a result to be significant, the p-value should be low.
• The p-value should be equal to or less than the alpha level used for significance testing.
• The t-critical is the t-score which has a p-value equal to the alpha level used.
• The confidence intervals (CI) tell us the likely range of the score in the population, or if the population is measured instead of the sample.
• The score we are referring to in the CI is the difference score, which is the summation of difference scores computed from the two groups/conditions.
• The CI provides us with the likely range of values of the difference score if the population is measured.
• These range of possible values cover the middle 95% of the cases in the distribution.
• If our result is statistically significant, it should be contained within the CI.
• The null hypothesis should not be contained within the CI for our result to be significant.
• The Wilcoxon test is used when data do not satisfy the assumptions of the repeated measures t-test.
• The Wilcoxon test is a non-parametric test that makes inferences about population parameters.
• The Wilcoxon test was developed by Frank Wilcoxon, who also developed the Wilcoxon-rank sum and the Wilcoxon signed ranks test.
• The rank-sum test is equivalent to the Mann-Whitney test, which is easier to calculate.
• When we refer to a Wilcoxon test, we refer to the Signed ranks test only.
• The Wilcoxon test deals with all data that can be ordered (ordinal data) and is easy to understand and calculate.
• The Sign Test only deals with data in the form of categories (nominal data) and is only used when the data are crude categories, rather than rich data of ranks or intervals.
• To use the Repeated Measures t-test, we need to make two assumptions about our data: the data are measured on a continuous (interval) level and the differences between the two scores are normally distributed.
• The Repeated Measures t-test makes no assumption about the distribution of the scores, only the differences between the scores.
• The Repeated Measures t-test is robust against violations of both assumptions about the distribution of the scores and the variances of the variables, given a sufficiently large sample.
• The Wilcoxon Test is a nonparametric test used to compare two groups.
• The Wilcoxon Test is used when the table can’t be used, for example when the sample size is bigger than the values given in the table.