Previously, we discussed how to apply the most basic hypothesis test: the z-test. It requires a relatively large sample size, and might be appreciated less by hackers searching for truth on a tight budget of time and money.

As an alternative, we briefly mentioned the t-test. The basic procedure still applies: form hypotheses, sample data, check your assumptions, and perform the test. This time though, we’ll run the test with real data from IoT sensors, and programmatically rather than by hand.

The most important difference between the z-test and the t-test is that the t-test uses a different probability distribution. It is called the ‘t-distribution’, and is similar in principle to the normal distribution used by the z-test, but was developed by studying the properties of small sample sizes. The precise shape of the distribution depends on your sample size. Continue reading “Statistics and Hacking: A Stout Little Distribution”