Hypothesis basics
This hypothesis represents the status quo and usually states that there is no effect or no difference in the population.
null hypothesis
This error occurs when a true null hypothesis is rejected.
Type I error
counterfactual argumentThis value is compared directly to the significance level to decide whether to reject the null hypothesis.
p-value
This test is used when the population variance is known and the population is normally distributed.
tests of the mean of a normal distribution
This test is used to evaluate claims about a population proportion when sample sizes are large.
population proportion
This hypothesis represents the claim or effect that the researcher wants to find evidence for.
alternative hypothesis
This error occurs when a false null hypothesis is not rejected
Type II error
This value defines the boundary between the rejection and non-rejection regions of a test statistic.
critical value
This test uses the standard normal distribution to evaluate a population mean.
Z-test
This test uses a normal approximation to test proportions when certain conditions are met
Z-test for proportions
This type of hypothesis specifies an exact value for a population parameter.
simple hypothesis
This value represents the probability of committing a Type I error.
significance level
This argument assumes the null hypothesis is true in order to evaluate how likely the observed data is.
counterfactual argument
This test is used when the population variance is unknown and the sample size is small.
the mean of a normal distribution
This test is used to evaluate whether the population variance equals a specified value.
variance of a normal population
This alternative hypothesis tests for a difference in one specific direction only.
one-sided composite alternative hypothesis
This term refers to the probability of committing a Type II error.
probability of a Type II error
This graph or mathematical relationship shows how the power of a test changes for different true parameter values.
power function
This distribution is used instead of the normal distribution when the population variance is unknown.
t-distribution
This distribution is used in hypothesis tests involving population variance.
chi-square distribution
This alternative hypothesis tests for a difference in either direction from the hypothesized value.
two-sided composite alternative hypothesis
This concept describes the probability of correctly rejecting a false null hypothesis.
power
This approach rejects the null hypothesis when the test statistic falls in the rejection region defined by α.
critical value approach
This value determines the shape of the t-distribution used in hypothesis testing.
degrees of freedom
This test is commonly used in quality control to check variability in manufacturing processes.
chi-square test for variance