An introduction to pooled-variance t tests and confidence intervals (in the setting of inference for two means). The shame/young offender data is simulated data with the same summary statistics as found in: Owen, T., Fox, S. (2011). Experiences of shame and empathy in violent and non-violent young offenders. The Journal of Forensic Psychiatry & Psychology, 22(4):551-563.

read moreI discuss the characteristics of the sampling distribution of the difference in sample means (X1 – X2). I then work through an example of a probability calculation that involves these concepts. The values for male and female heights are based on information in the 2009-2011 Canadian Health Measures Survey.

read moreHere I work through two examples of finding areas under the t distribution, using both R and the t table.

read moreA discussion of the assumptions of the t test on one mean. (The assumptions are the same as those of the t confidence intervals for one mean.) The assumptions are discussed, and the effect of different violations of the normality assumption is investigated through simulation.

read moreof a t test, then briefly investigate the influence of 3 outliers on the conclusions. The sleep misperception index data is simulated data with the same summary statistics as found in: Manconi et al. (2010). Measuring the error in sleep estimation in normal subjects and in patients with insomnia. Journal of Sleep Research, 19:478–486.

read moreAn introduction to t tests for one population mean. I briefly discuss when we use the test, and when we would use a z test instead. I also briefly discuss the hypotheses of the test, and the p-value for different alternatives. I then work through an example. (If you are comfortable with the basics of hypothesis testing, and understand the difference between t and z procedures from confidence intervals, then much of this video may be review.) If you are just looking for an example, it starts at 7:00. The reaction time data is simulated data...

read moreA look at what factors influence the power of a test. This discussion is in the setting of a one-sample Z test on the population mean, but the concepts hold for many other types of test as well. I discuss what factors affect power, and illustrate the concepts visually using various plots. There are no power calculations carried out in this video; I have another video on calculating power: http://youtu.be/BJZpx7Mdde4

read moreAn example of calculating power and the probability of a Type II error (beta), in the context of a two-tailed Z test for one mean. Much of the underlying logic holds for other types of tests as well.

read moreAn example of calculating power and the probability of a Type II error (beta), in the context of a one-tailed Z test for one mean. Much of the underlying logic holds for other types of tests as well.

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