Statistical Inference Basics

Statistical Inference Basics

1. Inferential statistics

   • Used to determine what happens if we conduct the same study multiple times
   • Statistically significant
     • Group differences are due to random error or the variable
   • Significance level
     • Also alpha level
     • How willing are you to be wrong
   • Research hypothesis
     • General hypothesis stating a relationship
   • Null hypothesis
     • States that the population means are equal

2. Example statistical tests

   • T test
     • Allows you to determine whether the mean difference came from the null hypothesis
     • Reflect all possible outcomes if we compare the means of the two groups and the null hypothesis is true
     • T = group difference/within group variability
     • Degrees of freedom
       • Adjustment used to account for the fact that we are estimating population values
   • One tailed test
     • When the research hypothesis specified a direction of difference
   • Two tail hypothesis
     • Research hypothesis did not specify a predicted direction of difference
   • F test
     • Used when there are more than two levels of an independent variable
     • Ratio of systematic and error variance
       • Systematic variance
         • Deviation of group mean from grand mean
       • Error variance
         • Deviation of individual scores from the group means

3. Errors

   • Type I errors
     • When we reject the null hypothesis, but it is true
   • Type II errors
     • When the null hypothesis is retained based on sample data, but is false
     • Worse than Type I errors

4. Power analysis

   • Related to the likelihood that you will retain a null hypothesis that is false

5. Confidence intervals

   • When they do not overlap, the results are most likely statistically significant

6. Conclusion validity

   • Extent to which the conclusions on the relationships are correct