Minitab t test
This can be useful for “before and after” scenarios.Įxample: Did the average monthly spend per customer significantly increase after I ran my last marketing campaign? With a paired t-test, you’re testing two dependent (paired) groups to see if they are significantly different. The classic example we’ve described above, where the means of two independent populations are compared to see if there is a significant difference.Įxample: Do Iowan shoppers spend more per store visit than Alaskan ones? Paired t-test Instead of a second population, you run a test to see if the average of your population is significantly different from a certain number or value.Įxample: Is the average monthly spend among my customers significantly more or less than $50? 2-sample t-test But there are some other common variations of the t-test worth knowing about too. So far we’ve talked about testing whether there’s a difference between two independent populations, aka a 2-sample t-test. The t-test will prove or disprove your null hypothesis.Ĭheck out our list of 10 books every market research leader should read in 2021 Different kinds of t-tests In a t-test, you start with a null hypothesis – an assumption that the two populations are the same and there is no meaningful difference between them. If that probability is very small, then you can be confident that the difference is meaningful (or statistically significant). They tell you what the probability is that the differences you found were down to chance. T-tests give you an answer to that question. Was your customer service really better in LA, or was it just chance that your LA sample group happened to contain a lot of customers who had positive experiences? Group A in Los Angeles gave you on average 8 out of 10 for customer service, while Group B in Boston gave you an average score of 5 out of 10. they didn’t just happen by a fluke).įor example, let’s say you surveyed two sample groups of 500 customers in two different cities about their experiences at your stores. Running a t-test helps you to understand whether the differences are statistically significant (i.e. The t-test, also known as t-statistic or sometimes t-distribution, is a popular statistical tool used to test differences between the means (averages) of two groups, or the difference between one group’s mean and a standard value.