11/19/2023 0 Comments T test power calculator![]() Lowering this number allows for higher confidence that the difference observed isn’t due to chance, but requires larger sample sizes. The default value is set to 0.05, but can be set within a range of 0.01 to 0.1. This is commonly referred to as a type 1 error or a false positive. ![]() The significance value is the probability that a statistically significant difference is detected, given no actual difference exists. Standard A/B test procedure is usually 50-50, but feel free to tune this to your own special circumstances. The default value of 0.5 sets the A/B test to split test users versus control users 50%/50%. On the other hand, a two-sided test is used to determine if the test variation is simply different than the control. HypothesisĪ one-sided test (recommended) is used to determine if the test variation is better than the control. The calculator is automatically set to optimal defaults, but you can adjust the advanced settings to see how they impact your results. How consistent will be based on your statistical power-an advanced input we’ll discuss below: Advanced calculations: Its output will be the minimum viable test size and minimum viable control size in order to consistently achieve statistically significant results. The calculator will use these values to determine the optimal sample size for your study. For example, if you are studying the effectiveness of a new UI, the minimum detectable effect would be the smallest increase in usage rate that you want to be able to detect. This is the smallest difference in the behavior or outcome that you want to be able to consistently detect with your study. For example, if you are studying the effectiveness of a new UI, the baseline conversion rate would be the percentage of people in your audience who are expected to use the desired features without being exposed to the new UI. This is the preexisting or expected conversion rate of your control group. How to use Statsig’s sample size calculator: Determine your baseline conversion rate In statistical terms, we want to detect the minimum detectable effect (MDE) with statistical confidence. This ensures the test is adequately powered to detect the change while minimizing statistical noise. It displays the df, critical value, ncp as well, so you can check all these calculations separately.Įdit: Using Satterthwaite's formula or Welch's formula doesn't change much (still 0.Setting up a proper A/B experiment involves selecting the appropriate sample size. This matches the result from G*Power which is a great program for these questions. With n1, n2, mu1, mu2, sd1, sd2 as defined in your question: > alpha dfGP cvGP muDiff sigDiff ncp 1-pt(cvGP, dfGP, ncp) # power SAS might use Welch's formula or Satterthwaite's formula for the df given unequal variances (found in this pdf you cited) - with only 2 significant digits in the result one cannot tell (see below).The true difference in means is typically taken as $\mu_ - 2$ as degrees of freedom for the $t$-distribution in this case (different variances, same group sizes), following a suggestion from Cohen as explained here. ![]() You're close, some small changes are required though: Ultimately, I would like to get an understanding that would allow me to look at simulations for more complicated procedures. Is this the correct approach? I find that if I use other power calculation software (like SAS, which I think I have set up equivalently to my problem below) I get another answer (from SAS it is 0.33). Here is the full script in R: #under alternative I calculated beta in the diagram above using the non central distribution and the critical value found above. ![]() I could compute the critical value under the null relating to having 0.05 upper tail probability: df<-(((sd1^2/n1)+(sd2^2/n2)^2)^2) / ( ((sd1^2/n1)^2)/(n1-1) + ((sd2^2/n2)^2)/(n2-1) )Īnd then calculate the alternative hypothesis (which for this case I learned is a "non central t distribution"). So I assumed that given the following about the two populations and given the sample sizes: mu1<-5 Here is a diagram that I found to help understand the process: I am trying to understand power calculation for the case of the two independent sample t-test (not assuming equal variances so I used Satterthwaite). ![]()
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