Hypothesis Testing Flashcards
Why do we use hypothesis testing?
To make decisions using data
What is a hypothesis?
Statement about one or more populations that we test using sample statistics
State the six steps of hypothesis testing?
- State the hypothesis
- Identify the appropriate test statistic
- Specify the level of significance
- State the decision rule
- Collect data and calculate the test statistic
- Make a decision
What is the null hypothesis?
Statement concerning a population parameter considered to be true unless the sample we use gives convincing evidence that the null hypothesis is false. We aim to reject the null-hypothesis.
What is the significance level?
The significance level reflects how much sample evidence we require to reject the null hypothesis
What are the four possible outcomes when we test a null hypothesis?
- Correct decision on rejecting H0
- Correct decision on not rejecting H0
- Type 1 Error (false positive); when we reject a true null hypothesis
- Type 2 Error (false negative);
when we fail to reject a false null hypothesis
What is a Type 1 Error?
False positive. Reject a true null hypothesis
What is Type 2 Error?
Fail to reject a false null hypothesis
How is the significance level shown?
With alpha. Its complement is (1 - a) which is the confidence level.
How can you reduce the probability of a Type 1 or 2 Error?
Larger sample size
What is the complement of the Type 2 Error?
Power of a test, denoted by Beta
What is a parametric test?
A statistical test that makes assumptions about the parameters of the population distribution (e.g., normality and homogeneity of variance). These tests are used when the data meet certain conditions, such as being normally distributed and measured on an interval or ratio scale.
What are four reasons to use a nonparametric test?
When the data do not meet distributional assumptions.
When there are outliers.
When the data are given in ranks or use an ordinal scale.
When the relevant hypotheses do not concern a parameter.
