UC Berkeley was once sued for bias against women applying to their graduate programs. Their fall admission figures showed a difference of 25% which couldn’t be just attributed to chance. They successfully defended against this charge with a drill down to department-level segmentation which proved that in fact women had a higher admission rate for most departments; it was just that majority of the female applicants had applied for competitive departments which already had a low admission rate for both genders hence skewing the overall data. Welcome to Simpson’s Paradox and how you can beat it using Post-Result Segmentation to find trends for a certain segment of traffic which could be buried under a mountain of data. Post Result Segmentation. Post Result Segmentation in VWO is an enterprise feature which lets you slice and dice your reports to smaller, specific and targeted segments. This lets you find out the most…
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