Let’s pose the null hypothesis that the two sets of data come from the same probability distribution (not necessarily Gaussian). Under the null hypothesis, the two sets of data are interchangeable, so if we aggregate the data points and randomly divide the data points into two sets, then the results should be comparable to the results obtained with the original data.
So, the strategy is to generate random datasets, with replacement (bootstrapping), compute difference in means (or difference in medians or any other reliable statistic), and then compare the resulting values to the statistic computed from the original data.
%% Hypothesis Testing clear; close all; clc; data1=randn(100,1); data2=(randn(150,1).^2)*10 + 20; all_data=[data1; data2];
- Null hypothesis is that the two distribution that we are sampling are from the same population.
%% First Sample mu1=mean(data1); %% Second Sample mu2=mean(data2); actualdiffmn=(mu1-mu2)
- Now, since our null hypothesis is that the two distribution comes from the same population, we can mix it to draw two samples again.
%% Using Randomization to test the hypothesis numsim = 10000; % number of simulations to run mn1=zeros(1,numsim); mn2=zeros(1,numsim); diffmn = zeros(1,numsim); for num=1:numsim % vector of indices (a random ordering of the integers between % 1 and n where n is the number of data points) indx = randperm(length(all_data)); data_sim=all_data(indx); data_sim1=data_sim(1:length(data1)); data_sim2=data_sim(1:length(data2)); mn1(num)=mean(data_sim1); mn2(num)=mean(data_sim2); diffmn(num)=(mn1(num)-mn2(num)); end % visualize figure; hold on; hist(diffmn,100); ax = axis; plot(repmat(actualdiffmn,[1 2]),ax(3:4),'r-'); pval = sum(abs(diffmn) > abs(actualdiffmn)) / length(diffmn); title(sprintf('Actualdiffmean = %.4f; p-value (two-tailed) = %.6f',actualdiffmn,pval)); legend('Results of simulation','Actual difference in means') xlabel('Difference in means'), ylabel('Frequency')
actualdiffmn = -28.5153
- For details see: Lectures on Statistics and Data Analysis in MATLAB
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