Statistical Testing (AQA A Level Psychology)
Revision Note
Written by: Claire Neeson
Reviewed by: Cara Head
Probability & significance
Psychologists are interested in finding out if the results of their studies show real differences or correlations, or if the results are due to chance
To determine whether results are significant and not due to chance, researchers use a measure of the level of significance
Researchers must decide how large an effect or relationship is required to conclude that the observed result is unlikely to be due to chance
This decision is reflected in the level of significance applied to the data
The level of significance is expressed as a decimal value where 'p' stands for the probability that chance factors are responsible for the results
For most purposes in psychology, the 5% level of significance is appropriate which is expressed as p < 0.05 (i.e. the probability of chance factors producing the observed result is less than or equal to 5%)
Psychologists may use a more stringent level of significance, e.g. p < 0.01 (i.e. the probability that the results have occurred by chance is less than or equal to 1%) in cases where:
there is a human cost, e.g. drug trials
where existing research evidence is contradictory
The researcher will then use statistical tables to find the critical value which will determine whether or not they can reject the null hypothesis
Use of statistical tables & the critical value
Once the researcher has conducted their research and carried out a statistical test, the test produces an observed (or calculated) value, which is used to determine whether the results of their study are significant
The observed/calculated value needs to be compared to the critical value in the critical values table to determine significance
To find the critical value from the table, the researcher must ask the following questions, which will help them to use the critical values table properly:
Examiner Tips and Tricks
In statistics, you will need to remember lots of different terms. Remember that a one-tailed hypothesis is the same as a directional hypothesis (the researcher predicts the result will go in one direction). A two-tailed hypothesis is the same as a non-directional hypothesis (the result could go in two directions).
Once the above questions have been answered, the researcher consults the critical values table to find the relevant critical value and then compares this to the observed/calculated value
To determine significance, there will be a statement underneath the critical values table which tells you whether the critical value needs to be more or less than the observed/calculated to be significant
E.g. "The calculated value of rho must be greater than or equal to the critical value to be significant"
Once significance is determined, a statement of significance will then be written out which should include:
whether the result is significant or not
the observed/calculated value compared to the critical value e.g. the calculated value of 0.39 is more than the critical value of 0.28
the N value (number of participants) e.g. N = 12
whether to accept or reject the null/alternative hypothesis e.g. accept the alternative hypothesis as the results are significant (not due to chance)
Type I & Type II Errors
A Type I Error occurs when the null hypothesis is rejected when it should have been accepted
The researcher claims that the results are significant when in fact they are not (also known as a ‘false positive’)
A Type I Error is more likely to happen when the researcher uses a probability value that is too stringent e.g.
0.1 rather than 0.05
0.06 rather than 0.05
A Type II Error occurs when the null hypothesis is accepted when it should have been rejected
The researcher claims that the results are not significant when in fact they are (also known as a ‘false negative’)
A Type II Error is more likely to happen when the researcher uses a probability value that is too lenient e.g.
0.01 instead of 0.05
0.03 instead of 0.05
Using a 0.05 significance level guards against making either a Type I or a Type II Error
Worked Example
Here is an example of an A02 question you might be asked on this topic.
AO2: You need to apply your knowledge and understanding, usually referring to the ‘stem’ in order to do so (the stem is the example given before the question).
Dr Stats has conducted a study which looks at whether or not a diet of oranges and beef can improve IQ compared to a normal diet. After conducting the study Dr Stats concludes that oranges and beef do indeed increase IQ significantly using a significance level of 0.10.
Q. With reference to the study outlined in the stem above explain why Dr Stats is likely to have made a Type I Error in his conclusion and suggest how he might avoid making this error in the future.
[3 marks]
Model answer:
Identify why a Type I Error has occurred:
A Type I Error is likely to have occurred because the significance level of 0.10 has been set too high; [1 mark]
Suggest how the error could be avoided:
Dr Stats could rectify the error by setting his significance level at 0.05; [1 mark]
Expand on your explanation:
Setting the significance level at 0.05 means that the probability of chance factors affecting the result is 5% or less which means that there is less leeway for claiming a significant result than with a level of 0.10; [1 mark]
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