Introduction to Inferential Testing

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Inferential Testing

The aim of inferential statistics is to discover if your results are statistically significant. A statistically significant result is one which is unlikely to have occurred through chance.

Null hypothesis

Usually, psychologists use the 5% level (0.05). This means that just 1 in 20 of results could have occurred due to chance.
We express our results in terms of the null hypothesis. If a result is statistically significant, we can reject the null hypothesis. If the result is not statistically significant we must accept the null hypothesis.
In other words, we need to be 95% confident that the results were due to the manipulation of the IV in order to reject the null hypothesis.

Common level of significance

In psychology, the most common level of significance is 5% (e.g. 1 in 20 or p≤0.05) (the p stands for probability and ≤less than or equal to).
This level is seen as a representing a balance between a type I and type II error.
The 5% level is fine for psychology, but clearly if we were experimenting on a new drug, a 1 in 20 chance that it might kill you are not very good odds and the level would have to be significantly reduced.

Type I error

False positive.
Rejecting the null hypothesis, when there is a possibility that the results were due to chance.
Often caused by using a significance level that is too lenient e.g. 10%, 0.10, 1 in 10, p≤0.10. Not being cautious enough.

Type II error

False negative.
Accepting the null hypothesis, when there is a possibility that the results were significant.
Often caused by using a significance level that is too strict e.g. 1%, 0.01, 1 in 100, p≤0.01. Being over cautious.

Critical value table

Once an inferential statistical test has been carried out, it is necessary to compare the calculated value against the critical value in a critical value table to see if the result is statistically significant and the null hypothesis can be rejected.

Exam question

A common question in the examination is to interpret a result using critical value tables, therefore make sure you have practiced this.
You will need to know if the study used a 1 or 2 tailed hypothesis (directional or non-directional). The significance level is generally 0.05.
For most tests, N = number of participants, however for Chi-squared you will need to calculate the degrees of freedom (df).

Directional vs non-directional hypothesis

A directional hypothesis says that the independent variable will have an effect on the dependent variable. It will state what that effect will be (in other words, the direction of results).
- E.g. Eating cheese before bed will disrupt a person's sleep.
A non-directional hypothesis just says that the independent variable will affect the dependent variable BUT it does not predict how the IV will affect the DV.
- E.g. Eating cheese before bed will affect a person's sleep.