DC-Den

You received glass window samples from two different vendors. You wish to check if the thickness of the samples are the same. Based on the samples, you obtain the following data: Vendor Sample Sample 1 Sample 2 Mean (cm) 2.0 2.3 Standard Deviation (cm) 0.2 0.3 Number of Samples 15 20 At first glance sample 2 is 0.3cm thicker than sample 1. But are their thickness significantly different? To make a comparison you would need a perform a Two Sample Mean test . Two Sample Mean Test A two sample mean test compares two sample distribution means against each other. It differs from the one sample mean test that compares against a target value. We write the Null Hypothesis as the means of sample 1 and of sample 2 are equal. `H_0: mu_1 = mu_2` And the Alternative Hypothesis as the mean of sample 1 and of sample 2 are not equal. `H_1: mu_1 != mu_2` We could also test

In the previous blog, I implemented a One Sample Mean hypothesis test in Excel LAMBDA. When given an array of sample data, we firstly calculate the sample mean, standard deviation and size, before passing these into the function. The function compares a sample mean against an expected population mean. In this blog, we will create a new function to take the sample data array directly, calculate the necessary statistics, and then reuse the One Sample Mean function to perform the hypothesis test. This extension ensures the same result regardless of the approach used. Parameter Changes In the earlier One Sample Mean dcrMean.One.TTest the input parameters were defined as: dcrMean.One.TTest =LAMBDA(expected_mean, sample_mean, sample_stdev, sample_size, [tail], [show_details], To implement One Sample Mean test for array,  dcrMean.One.TTest.Array  input parameter will take the entire data array: dcrMean.One.TTest.Array =LAMBDA(expected_mean, array, [tail], [show_details], Within this