# Which statistical test should I use to compare two samples?

I have two groups and must conduct hypothesis testing by using an appropriate stats test. Seeing as I have 26 participants in each group, do not have the standard deviation (or any other information other than the data points for each group), I thought that the t-test would be the best choice.

null hypothesis = there is no significant difference between both groups alt hypothesis = such a difference exists

Is there another statistical test that could be performed (like the z test or Fmax)? Is the t-test the only hypothesis testing that I should be doing here, or could I add another one?

Thank you

• What hypothesis do you wish to test?
– whuber
Dec 1, 2014 at 0:59
• The null hypothesis is that there is no significant difference between both groups, and the alt hypothesis is that there is such a difference. Is this specific enough? Thank you @whuber Dec 1, 2014 at 1:13
• The word "significant" doesn't really belong in hypotheses, which are statements about populations. If you're interested in "no difference" vs "any kind of difference", that sounds more like a two-sample goodness of fit test. What are you actually trying to find out? What's the underlying question you're trying to address? Dec 1, 2014 at 1:16
• I'm afraid it is not sufficiently specific: you need to state in what way you want to measure any differences. Are you concerned about differences of means? Medians? Variances? Distributions? Something else? (Incidentally, a valid hypothesis can make no reference to "significance": that is a matter of how the hypothesis is evaluated.)
– whuber
Dec 1, 2014 at 1:17
• @Glen_b This is all for a research paper in which my study attempts to find out wether or not there's a significant difference between the failure rate of a sample of students who regularly watch Netflix and a sample of students who don't watch it at all. My data points represent the percentage of tests failed all throughout high school in both groups. I formed the hypothesis myself, and so the goal of this study. Could that be where the mistake is? How can I reformulate to make it work with a t-test? Thanks Dec 1, 2014 at 1:31