Let's actually increase the sample size to 1000000, as shown here:

A = np.random.normal(25.0, 5.0, 1000000) 
B = np.random.normal(25.0, 5.0, 1000000) 
 
stats.ttest_ind(A, B) 

Here is the result:

What does that do? Well, now, we're back under 1 for the t-statistic, and our value's around 35 percent.

We're seeing these kind of fluctuations a little bit in either direction as we increase the sample size. This means that going from 10,000 samples to 100,000 to 1,000,000 isn't going to change your result at the end of the day. And running experiments like this is a good way to get a good gut feel as to how long you might need to run an experiment for. How many samples does it actually take to get a significant result? And if you know something about the distribution of your data ahead of time, you can actually run these sorts of models.