Stats final review chapter 9
Hypothesis
is a statement about a population parameter subject to verification. in statistical analysis we make a claim, that is, state a hypothesis, collect data, and then use the data to test the claim.
Hypothesis testing
is a procedure based on sample evidence and probability theory to determine whether the hypothesis is a reasonable statement.
Null hypothesis
denote by 𝐻0, is a statement about the value of a population parameter developed for the purpose of testing numerical evidence.
Alternative hypothesis
denoted by 𝐻1, is a statement that is accepted if the sample data provide sufficient evidence that the null hypothesis is false.
Level of significance
is the probability of rejecting the null hypothesis when it is true. the level of significance is designated 𝛼, the Greek letter alpha. it is also sometimes called the level of risk.
Test statistic
is a value, determined from sample information, that is used to determine whether to reject the null hypothesis.
Decision rule
is a statement of the specific conditions for which the null hypothesis is rejected and of the conditions under which it is not rejected.
Type I Error
is rejecting the null hypothesis when it is true. the probability of a Type I error is denoted by the Greek letter “”, also known as the significance level of a test.
Type II Error
is failing to reject the null hypothesis when it is false. the probability of a Type II error is denoted by the Greek letter “𝛽”.
P-value
is the probability of observing a sample value as extreme as, or more extreme than, the value observed, given that the null hypothesis is true