"Mastering Sampling Distribution for Accurate Statistical Inference"

Sampling Distribution

Sampling distribution refers to the distribution of a statistic (such as mean or proportion) calculated from multiple samples of the same size from a population. It is important in statistical inference as it helps to estimate the population parameter and determine the level of confidence in the estimate.

In AB testing on web and in mobile, sampling distribution plays a crucial role in determining the statistical significance of the results. The sample size and the variability of the data collected from the samples affect the shape and spread of the sampling distribution. A larger sample size and lower variability result in a narrower and more normal-shaped distribution, which increases the accuracy and reliability of the test results.

By understanding the sampling distribution, AB testers can determine the appropriate sample size and level of significance to achieve the desired statistical power and minimize the risk of type I and type II errors. They can also use techniques such as bootstrapping and simulation to generate a sampling distribution and estimate the confidence intervals and p-values of the test results.