1. Setting significance levels at the correct level
Type1 and Type 2 Errors
Setting significance levels at the correct level

2. Aim of Psychological Research
When we have finished analysing research results and we have tested for significance, we make a statement that we must accept or reject the null hypothesis at the set level of significance, usually p<0.05.
We may be right or wrong. We can never be absolutely certain that an apparent effect is not a fluke.

3. If a researcher claims support for the research hypothesis with a significant result when, in fact, variations in results are caused by random variables alone, then a TYPE 1 ERROR is said to have occurred.
Through poor design or faulty sampling, researchers may fail to achieve significance, even though the effect they were attempting to demonstrate actually does exist. In this case it would be said that they had made a TYPE 2 ERROR.

4. Summary of Errors Accepted Rejected True  Type 1 error False
Null Hypothesis is:
Accepted
Rejected
True 
Type 1 error
False
Type 2 error
Null Hypothesis is actually:

5. Probability as measured on a scale from 1 - 10
Standard level for psychology research
0.05
0.5 1
Will
Never
Happen
Will happen 50% of the time
Will Definitely Happen
Will Happen5 Times in 100
More chance of Type 2 Error
More chance of Type 1 Error

6. Significance levels
If significance levels are set too loosely (i.e. towards 0.1) there is more chance of rejecting the null hypothesis and therefore causing a TYPE 1 error.
If significance levels are set too strictly (i.e. towards 0.01) there is more chance of accepting the null hypothesis and therefore causing a TYPE 2 error.

7. Reason we set a standard of 0.05
This value is in the middle of the 2 sides of significance and therefore reduces the chances of Type 1 and Type 2 errors.

8. Exam question (2 marks) Why do we use the 0.05 level of significance?
The reason the experimenter ha used 0.05 as the level of significance is that it is the standard set level for psychological research and it reduces the chances of type 1 and type 2 errors occuring.

9. Exam Question (2 marks)
Explain what is meant by the phrase ‘the difference…was not statistically significant at the 5% level’.
The null hypothesis is true meaning there is a high probability that chance factors were responsible for observed differences. The researcher cannot be 95% sure that the difference has occurred because one group is ‘depressed’ and the other ‘not-depressed’/there is more than a 5% probability of these results occurring by chance.