Statistical tests help determine whether the outcome of an experiment could have been accidental.
Vantage Analytics Library contains classical parametric and nonparametric statistical tests and more recently developed statistical tests. Using the groupby parameter, you can analyze data groups defined by selected variables with specific values, thereby running multiple tests simultaneously to produce a profile of customer data showing hidden clues about customer behavior.
| Approach | Description |
|---|---|
| Bayesian estimation | Given experimental outcome, infers conclusions from posterior judgments about parameters. |
| Likelihood | Given experimental outcome, infers conclusions from likelihood function of parameters. |
| Hypothesis testing | Uses either of the following:
|
All statistical tests in Analytics Library use hypothesis testing. They belong to the classes in the following table.
Each class has many variants, some of which are named for their originators. Tests with multiple originators may have multiple names. Tests can be applied to one, two, or multiple samples of data. The specific hypothesis of the test may be two-tailed, upper-tailed or lower-tailed.
| Hypothesis Test Class | Test Names |
|---|---|
| Parametric Tests |
|
| Nonparametric Binomial Tests |
|
| Nonparametric Kolmogorov-Smirnov Tests |
|
| Nonparametric Tests Based on Contingency |
|
| Nonparametric Rank Tests |
|
Hypothesis tests depend on assumptions made in the context of the experiment. Be sure the tests are valid in the context of the data to be analyzed. For example, is it a fair assumption that the variables are normally distributed? The choice of test depends on the answer to this question. An inappropriate test can reject or accept the null hypothesis incorrectly, causing false alarms or misses, respectively.