During his Ph.D. research, mathematician Tyron Lardy worked on a new approach to hypothesis testing. Instead of the ...

Today our goal is to cover hypothesis testing and the basic z-test, as these are fundamental to understanding how the t-test works. We’ll return to the t-test soon — with real data.

Hypothesis testing begins when a researcher observes a pattern in a set of data. The test determines whether the pattern can be explained by coincidence or other variables. The methodology used ...

P[Type I error]=Alpha and P[Type II error]=Beta. In the next examples Type I and Type II errors will be calculated in hypothesis tests for a population mean. In all the following examples assume that ...

T-tests are used when the data sets follow a normal distribution and have unknown variances, like the data set recorded from flipping a coin 100 times. The t-test assists in hypothesis testing in ...

In Hypothesis Testing 1, 2 and 3, you have used normal and t-distributions to test hypotheses. Chi-Square tests use the Chi-Square probability distribution. You will be introduced to the use of that ...

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is most often used by researchers to test predictions, called hypotheses. The first step in ...

Define a test statistic, level of significance, and the rejection region for a hypothesis test. Give the form of a rejection region. Perform tests concerning a true population variance. Compute the ...

Most scientists use two closely related statistical approaches to make inferences from their data: significance testing and hypothesis testing. Significance testers and hypothesis testers seek to ...

The answer may be what some are calling "winter break hypothesis." While unproven ... As of this writing, others are busy running tests, and the results are inconclusive. This episode is a ...