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1. Types

   • Numeric
   • Ordinal
   • Categorical/Nominal

2. Tests

   • Comp: 2 Groups
     • Unpaired T Test
     • Paired T Test
     • Fisher's Test/Chi Square
       • 1. Look at the Crosstabulation table. This table shows the dispersal of the predictor variable across levels of the outcome variable. 2. Interpret the Pearson Chi-Square p-value.  3. If researchers have a significant p-value, then they can interpret the first row in the Risk Estimate table. The unadjusted odds ratio is presented in the Value column and the lower and upper limits of the 95% confidence interval wrapped around the odds ratio.  If the p-value is significant and the odds ratio is above 1.0 along with the confidence interval, then the treatment group is MORE LIKELY to have the outcome. If the p-value is significant and the odds ratio is below 1.0 along with the confidence interval, then the treatment group is LESS LIKELY to have the outcome. If the p-value is non-significant, then researchers will see that the 95% confidence interval crosses over 1.0. They can report the odds ratio or p-value as needed. Fisher's Exact Test is used when there are less than five (5) observations in one of the cells of the 2x2 table.  This is known as the Chi-square assumption. Fisher's Exact Test is also used when there are less than 20 observations being tested.
     • Mann-Whitney Test
     • Wilcoxon Test
     • McNemar's Test
   • Comp: 1 Group to Hypothetical Value
     • Chi Squared /Binomial Test
       • The Chi-square goodness-of-fit test is used to answer research questions about the dispersal of a categorical outcome across a given population.  1. Researchers first hypothesize the proportion of the population that will "fit" into each level of the categorical outcome. Then, the researcher tests the observed proportions in each level against the hypothesized proportions in each level to see if they are significantly different. For instance, let's say that researchers hypothesize that 90% of a population will possess a certain characteristic. After collecting data from a representative sample from the population, however, they find that only 67% of the population has the characteristic. Chi-square goodness-of-fit generates evidence that the observed proportion (67%) was statistically different from the hypothesized proportion  (90%) with an effect size of 23% (90% - 67% = 23%).If the p-value is LESS THAN .05, then the observed proportion was significantly different than the hypothesized proportion.If the p-value is MORE THAN .05, then the observed proportion was NOT significantly different than the hypothesized proportion. 
     • Wilcoxon Test
     • One Sample T Test
   • Comp: 3 More Groups
     • Repeated Measures Anova
     • Friedman Test
     • One-way ANOVA
     • Kruskal-Wallis Test
     • Chi-Square Test
   • Measure: Association of 2 Variables
     • Spearman Correlation
     • Contingency Coefficients
     • Pearson correlation
   • Prediction
     • Simple Linear Regression
     • Non-Parametric Regression
     • Multiple Linear/Non Linear Regression
     • Multiple Logistic Regression
     • Proportional Odds Regression

3. Probability

   • P(E)= Number of Favorable/ Total Possible Outcomes
   • Conditional
     • Bayesian Theorem
   • Permutations
   • Recombinations
   • Dependent
   • Independent