User funnel indicates a series of steps taken by users through the site. It's much better to use a metric that's less optimal than it is to come up with the perfect metric for your test. If you are running a suite of A/B tests, it is preferable to have a metric that works across the entire suite. If you're running a whole suite of AB tests, then ideally you'd have one or more metrics that you can use across the entire suite. Hard to define and get everyone to agree.Overall Evaluation Criterion (OEC): a term that Microsoft uses for when they come up with a weighted function that combines all of these different metrics. Summarize individual data measurements into a single metric: a sum or a count, an averageįor evaluation, you can choose either one metric or a whole suite of metrics.Nitty gritty details: How do you define what active is? Which events count towards activity?.A high level concept for a metric/ one sentence summary: "active users", "click-through probability".Detailed metrics: user experience with the product.High level business metrics: how much revenue you make, what your market share is, how many users you have.Do you have the same number of users across the two?.Invariant checking (sanity checking): Metrics that shouldn’t change between your test and control.Based on this, one would launch the new version. Since the minimum confidence limit is greater than 0 and the practical significance level of 0.02, we conclude that it is highly probable that click through probability is higher than 0.02 and is significant. Se_pool = sqrt(p_pool*( 1-p_pool)*( 1/N_cont + 1/N_exp))ĭ_min = 0.02 # Minimum practical significance value for difference N_cont = 10072 # Control samples (pageviews)
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