Biometry Chapter 5

1.

Null hypothesis significance test

Procedures used to decide whether chance alone can account for apparent patterns in our data

2.

Null hypothesis

A statement embodying the idea that there is no pattern in the data or no difference between samples or no relationship between variables.

(H_{0})

3.

Research hypothesis

An educated guess at the answer to a question about cause, mechanism, or function

4.

Statistical hypothesis

A hypothesis that states the specific relationships between variables and therefore parallels a prediction generated by a research hypothesis.

5.

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.

(H_{1})

6.

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)

7.

Degrees of freedom

One less than the sample size.

8.

Critical values

Values of statistics corresponding to a specific critical significance level and degrees of freedom. They can be looked up in a critical-value table.

9.

P-value / significance level

The probability of finding the observed, or more extreme, results
when the null hypothesis (H_{0}) of a study question is true.

10.

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

11.

Type I error

Rejecting a true null hypothesis

12.

Type II error

Accepting a false null hypothesis

13.

Power of a (statistical) test

The probability of rejecting a false null hypothesis. Or the probability of not making an error.

14.

Parametric test

Null hypothesis significance testing techniques for which data must meet special criteria. The data must have a normal distribution

- Use a t-Test

15.

Nonparametric test

Null hypothesis significance testing techniques that require fewer assumptions and do not rely on any distribution, normal or otherwise.

- Use Chi-square