Analysis | Visual | Segment | Profile
This new feature allows comparing the mean values on the observable variables on the segments defined by the Breakout variable.
Null Value Assessment
When two segments are selected, this option allows estimating if the mean values of the segments are significantly different.
Two tests are proposed for answering this question:
- a Frequentist one, NHST t-test, the Null Hypothesis Significance Testing with the Welch's two-sample, two tailed t-test, and
- a Bayesian one, BEST, described in the paper by John K. Kruschke, "Bayesian Estimation Supersedes the t-test", Journal of Experimental Psychology: General, 2013.
Below is the Bayesian network used in the BEST approach. We are assuming that the samples follow a Student's t-distribution. The segment and the benchmark have their ownand , but they share the same .
The default Confidence Level has been set to 95%. This is the same for the both tests.
As for the Bayesian test, the Region of Practical Equivalence (ROPE) on the Effect size around the null value has been set by default to [-0.1, 0.1].
The null value is declared to be rejected if the 95% Highest Density Interval (HDI) falls completely outside the ROPE.
You can use Window | Preferences | Tools | Statistical Tools to modify:
- the confidence level (for both the t-test and BEST),
- the Monte Carlo Marco Chain parameters that are used for inference in the Bayesian network described above,
- the ROPE size that defines an interval centered at 0, i.e. 0.2 defines the interval [-0.1, 0.1]