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This new feature allows comparing the mean values of the observable variables on the segments defined by the Breakout variable. 


Example

Let's take the Perfume example for which we have defined five segments with the Breakout Variable Product, namely Prod3, Prod4, ProdG1, ProdG5 and Prod G6.

Null Value Assessment

When two segments are selected, this option allows estimating if the mean values of these 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 two segments have their own and , but they share the same .


The default Confidence Level has been set to 95%. This is the same for 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 the Preferences to modify:

  • the confidence level (for both the t-test and BEST),
  • the Monte Carlo Markov 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].

Example

We first select Prod3 and ProdG5. Upon checking Null Value Assessment, the computation of both tests is triggered.

When the mean values are estimated as significantly different, a square is added next to the label:

  • for t-test
  • for BEST