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Hi all, and welcome to the first podcast episode focused on conducting inferential testing! Today's episode is going to spend about the first 30 minutes reviewing our hypothesis testing steps. Yes, it's a lot of covering what we already did in the last episode, but I think this is one of those areas of statistics that really benefits from additional review. After reviewing concepts like significance, critical values, and test statistics (as well as when to reject our null hypothesis), we will move into discussion of the one-sample z-test (or confusingly in SPSS, the one-sample t-test). This test is all about comparing a population and a sample, which is not something we do often in the social sciences. But when we do know about our population and we want to evaluate whether a sample of that group is different, this is the test we use. I hope the episode is easy to follow but if not email me and let me know!
By Jennifer MillerHi all, and welcome to the first podcast episode focused on conducting inferential testing! Today's episode is going to spend about the first 30 minutes reviewing our hypothesis testing steps. Yes, it's a lot of covering what we already did in the last episode, but I think this is one of those areas of statistics that really benefits from additional review. After reviewing concepts like significance, critical values, and test statistics (as well as when to reject our null hypothesis), we will move into discussion of the one-sample z-test (or confusingly in SPSS, the one-sample t-test). This test is all about comparing a population and a sample, which is not something we do often in the social sciences. But when we do know about our population and we want to evaluate whether a sample of that group is different, this is the test we use. I hope the episode is easy to follow but if not email me and let me know!