A test in which you compare the known population mean to a sample mean over a known standard deviation

A test used in designs that compare pre- and post- data or longitudinal data within a sample.

Uses Cohen's d and can be uni- or bi-directional.

A strategy that decreases the type 2 error rate.

Two sets of observations where each observation in one set has a special correspondence
or connection with exactly one observation in the other data set.

A test in which you compare the difference between two related samples.

A test used in designs that compare between group data.

A formula for an Independent T-Test, specifically.

Rejecting H knot when you shouldn't have.

When we collect a su ciently large sample of n independent observations from a population
with mean   and standard deviation  , the sampling distribution of  x will be nearly normal

A test in which you compare the difference between two independent samples.

A test that compares the average age of a sample to the average age of the United States population with a known standard error.

A formula or model for a In/Dependent T-Test.

The probability of correctly rejecting H knot.

The data are not dependent within and between the two groups,
e.g. the data come from independent random samples or from a randomized experiment.

A test in which you compare the known population mean to a sample mean over a unknown standard deviation.

A test that compares the weight of a sample of males ages 35 - 65 taking a diet pill placebo to a similar sample taking an experimental drug?

Has a p-value based on the t-value and t-distribution.

Statistical test that involves hypothesis testing (evaluating some idea about a population using a sample) and estimation (estimating the value or potential range of values of some characteristic of the population based on that of a sample).

A test that compares group level means.

A test that compares the average satisfaction of all participants with a yearly conference conducted every year for 20 with the average satisfaction of a sample of participants for 2 years.

Calculates t = (M - 0) / SE or M/SE.

Your free points.