JB An introduction to Welch (unpooled variance) t tests and confidence intervals. (Inference for two means.) The Cairo traffic police officer data is simulated data with the same summary statistics as found in: Kamal, A., Eldamaty, S., and Faris, R. (1991). Blood level of Cairo traffic policemen. Science of the Total Environment, 105:165-170.
I work through an example of a Welch (unpooled variance) t test and confidence interval (in the setting of inference for two means). The antivenom/swelling data is found in: Offerman et al. (2009). Subcutaneous crotaline Fab antivenom for the treatment of rattlesnake envenomation in a porcine model. Clinical Toxicology, 47: 61-68.
I discuss the assumptions of both the pooled-variance and Welch (unpooled variance) t tests and confidence intervals, and their advantages and disadvantages. I illustrate some of pros and cons using the results of two simulations.
An introduction to paired-difference procedures. I briefly discuss how paired difference scenarios arise, and briefly outline how we can use the paired-difference t procedure to construct confidence intervals and carry out hypothesis tests. (Paired difference procedures are sometimes referred to as matched-pairs procedures, depending on the setting.) The alcohol/reaction time data is loosely based on … Read more
An example of a paired-difference t test and confidence interval. The data used in this video is from: Penetar et al. (2012). The isoflavone puerarin reduces alcohol intake in heavy drinkers: A pilot study. Drug and Alcohol Dependence, 126:256-261. Values used in this video are simulated values based on the summary statistics found in the … Read more
A discussion of the assumptions of pooled-variance t tests and confidence intervals for the difference in means. The assumptions are briefly discussed, and the effects of different violations of the normality assumption are investigated through simulation. The quick summary: Pooled-variance t procedures are more robust to violations of the normality assumption than their one-sample counterparts.
An introduction to inference procedures for a single proportion. I discuss confidence intervals and hypothesis testing methods for a single proportion, based on the normal approximation. This video does not do an example of the calculations — I work through an example in another video.
A discussion of the sampling distribution of the sample proportion. I discuss how the distribution of the sample proportion is related to the binomial distribution, discuss its mean and variance, and illustrate that the sample proportion is approximately normally distributed for large sample sizes.
I work through an example of a confidence interval and a hypothesis test for a single proportion, using normal approximation methods (Z test and confidence interval). The male birth rate data is from: Koshy et al. (2010). Parental smoking and increased likelihood of female births. Annals of Human Biology, 37(6): 789–800.
I discuss determining the minimum sample size required to achieve a given margin of error when estimating a population proportion. (For intervals based on the normal approximation.)