What is a good minimum detectable effect?

Given sample size and sample variance, we can calculate the smallest real effect size which we would be able to detect at 80\% power. This value is called the minimal detectable effect with 80\% power, or 0.8 MDE. Ensuring we can get sufficient power is a critical step in experiment design.

What does detectable effect mean?

The minimum detectable effect represents the relative minimum improvement over the baseline that you are willing to detect in an experiment to a certain degree of statistical significance. It can help you figure out the likely relationship between impact and effort—or cost and potential value—for your experiment.

What does minimum effect mean?

The minimum detectable effect (MDE) is the effect size which, if it truly exists, can be detected with a given probability with a statistical test of a certain significance level. The MDE is inversely related to the significance threshold – the lower the p-value becomes, the larger the minimum detectable effect gets.

How do you read MDE?

Basically, MDE measures the experiment sensitivity. Highly sensitive settings, or low MDE, come along with a big sample size. The lower MDE, the more traffic you need to detect minor changes, hence the more money you have to spend on driving that traffic.

What is minimum detectable difference?

Abstract. The minimum detectable difference (MDD) is a measure of the difference between the means of a treatment and the control that must exist to detect a statistically significant effect. It is a measure at a defined level of probability and a given variability of the data.

What affects significance?

A statistically significant result isn’t attributed to chance and depends on two key variables: sample size and effect size. Effect size refers to the size of the difference in results between the two sample sets and indicates practical significance.

What is the difference between practical significance and statistical significance?

While statistical significance shows that an effect exists in a study, practical significance shows that the effect is large enough to be meaningful in the real world. Statistical significance is denoted by p-values whereas practical significance is represented by effect sizes.

What term is described as the minimal effect of interest in power analysis?

Alias: MEI. The minimum effect of interest is the effect size we would be happy/excited to find by using a statistical test to analyze a randomized controlled experiment, a.k.a. an A/B test. It is usually denoted μ1.

What does it mean when results are not statistically significant?

This means that the results are considered to be „statistically non-significant‟ if the analysis shows that differences as large as (or larger than) the observed difference would be expected to occur by chance more than one out of twenty times (p > 0.05).

What is the minimum sample size for statistical significance?

100
Most statisticians agree that the minimum sample size to get any kind of meaningful result is 100. If your population is less than 100 then you really need to survey all of them.

Can you have statistical significance but not practical significance?

Practical significance is related to whether common sense suggests that the treatment makes enough of a difference to justify its use. It is possible for a treatment to have statistical​ significance, but not practical significance.