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Independent Groups Condition
When directly comparing two proportions/means, the groups must be independent of each other thus have no effect on each other (e.g. can’t have husbands and wives included)
Pooling
A technique in which the data from two independent groups are combined into one larger group to more reliably estimate the common PROPORTION that they hypothetically if a null hypothesis of no difference between the two groups is true…NOT for means!
Paired Data
Observations that are collected in pairs or for which one group is naturally related to the other group, such as before/after treatment.
Paired Data Condition
The condition of relationship between the two groups of data that must be met for the use of a paired t-test…groups should NOT be independent of each other.
Chi-Square Model
A family of distributions whose shape are skewed right, becoming less skewed with increasing degrees of freedom.
Chi-Square Test of Goodness-Of-Fit
A test of whether or not the distribution of counts for ONE categorical variable from ONE population matches the distribution predicted by a model.
Chi-Square Test of Homogeneity
A test of whether or not the distribution of counts for ONE categorical variable from MULTIPLE populations match each other.
Chi-Square Test of Independence
A test of whether or not the distribution of counts for TWO categorical variables from ONE population influence each other.
Counted Data Condition
In order to use the Chi-Squre test, the data must be counts (or frequencies) for the various categories of the categorical variable…not proportions or means!
Expected Cell Frequency Condition
In order to use the Chi-Square test, at least 5 counts in each cell are required
Chi-Square Component
The squared standardized residual for each cell of a contingency table for use in a Chi-Square Model.
Chi-Square Statistic
The sum of the Chi-Square components for use in a Chi-Square model…a larger Chi-Square Statistic implies that the data is farther from the null hypothesis model.
