Biometry Chapter 5
Null hypothesis significance test
Procedures used to decide whether chance alone can account for apparent patterns in our data
A statement embodying the idea that there is no pattern in the data or no difference between samples or no relationship between variables.
An educated guess at the answer to a question about cause, mechanism, or function
A hypothesis that states the specific relationships between variables and therefore parallels a prediction generated by a research hypothesis.
Statistical alternative hypothesis
The reverse statement of the statistical null hypothesis. Stating that there is a pattern, or difference or relationship between the data sets.
Critical significance level
This sets the decision point determining whether a null hypothesis is accepted or rejected. It is expressed as a probability. 5% is often used. (α)
Degrees of freedom
One less than the sample size.
Values of statistics corresponding to a specific critical significance level and degrees of freedom. They can be looked up in a critical-value table.
P-value / significance level
The probability of finding the observed, or more extreme, results when the null hypothesis (H0) of a study question is true.
How do you decide to accept or reject the null hypothesis using the P-value?
Reject the null hypothesis if: P ≤ α
Accept the null hypothesis if: P > α
Type I error
Rejecting a true null hypothesis
Type II error
Accepting a false null hypothesis
Power of a (statistical) test
The probability of rejecting a false null hypothesis. Or the probability of not making an error.
Null hypothesis significance testing techniques for which data must meet special criteria. The data must have a normal distribution
- Use a t-Test
Null hypothesis significance testing techniques that require fewer assumptions and do not rely on any distribution, normal or otherwise.
- Use Chi-square