BRIEF DESCRIPTION: The One-Sample t-test is for continuous scaled data and it compares an observed sample mean with a predetermined value. For example, is our customer satisfaction sample mean significantly different from a pre-set figure such as an industry benchmark or an action standard. It also helps us to answer a question such as “Are we 95% confident that the mean score is between 7.5 and 8.5”. The t-test is a parametric procedure. SIMILAR STATISTICAL PROCEDURES One-sample z-test Non-parametric counterparts of the one-sample t-test include the Wilcoxon [READ MORE]
BRIEF DESCRIPTION: The Two-independent sample t-test is for continuous scaled data and it compares the observed mean on the dependent variable between two groups as defined by the independent variable. For example, is the mean customer satisfaction score (on the dependent variable) significantly different between men and women (on the independent variable). The t-test is a parametric procedure. SIMILAR STATISTICAL PROCEDURES Non-parametric counterparts of the Two-independent t-test include the (Wilcoxon) Mann-Whitney U-test (non-parametric), Wald-Wolfowitz Runs [READ MORE]
With so many statistical procedures available, how do we decide which tests are best to address our research objectives? (several posts deal with this topic). First and foremost, the decision as to which statistical procedures to apply to the data should be made BEFORE the design of the data collection instrument (e.g. the questionnaire), and not AFTER data has been collected. Plan ahead so that your analysis are entirely focused on addressing your research objectives and NOT to address your data. Too many researchers remain guilty of waiting to see the data so they can decide what to [READ MORE]
