We can also use the resampling approach to test the hypothesis with non-Gaussian datasets. Resampling is a technique for sampling from an available data set to generate additional samples, each of which is considered representative of the underlying population.

Approach description

Let's describe the most simple resampling method to check main hypothesis that two datasets X and Y have equal mean values:

• Concatenate both arrays (X and Y) into one big array;

• Shuffle that entire array so observations from each group are spread randomly throughout that array instead of being separated at the breaking point;

• Arbitrarily split the array in the breaking point (X_length), assign observations below index len(X_length) to Group A and the rest to Group B;

• Subtract the mean of this new Group A from the mean of the new Group B. This would give us one permutation test statistic;

• Repeat those steps N times to simulate the main hypothesis distribution;

• Calculate test statistics on initial sets X and Y;

• Determine critical values of the main hypothesis distribution;

• Check if the test statistic calculated on initial sets falls into a critical area of the main hypothesis distribution. If it falls then reject the main hypothesis.

Let's apply this approach in code: