Q: What does p < 0.05 actually mean in Psychology?

p < 0.05 means that there is less than a 5% probability that the observed results are due to chance alone. In other words, we are 95% confident that the results reflect a real effect. If p is less than 0.05, we reject the null hypothesis and accept the alternative hypothesis. However, this does not mean the effect is large or important; it only indicates statistical significance. Always interpret the result in the context of the study.

Q: Can I use a bar chart for continuous data?

No, bar charts are for discrete or categorical data (e.g., types of therapy). For continuous data (e.g., reaction times in milliseconds), you should use a histogram (no gaps between bars) or a frequency polygon. Using the wrong graph can lose marks. Always label your axes with units and include a title.

Q: What is a Type I error and how can I avoid it?

A Type I error occurs when you reject the null hypothesis when it is actually true (a false positive). This can happen if your significance level is too lenient (e.g., p < 0.10). To avoid it, use a stricter significance level like p < 0.05 or p < 0.01. Also, ensure your test is appropriate for your data and that you have not conducted multiple tests without correction (e.g., Bonferroni). In the exam, mention that a Type I error is more likely with a one-tailed test if the direction is wrong.

Question 1

How do I know which statistical test to use in AQA A-Level Psychology?

Accepted Answer

You need to consider three things: the experimental design (independent groups, repeated measures, or matched pairs), the level of measurement (nominal, ordinal, or interval), and whether you are looking for a difference or a correlation. For example, if you have a repeated measures design with ordinal data looking for a difference, use the Wilcoxon signed-ranks test. For independent groups with ordinal data, use Mann-Whitney U. For nominal data, use Chi-Square. For correlations, use Spearman's rho (ordinal) or Pearson's r (interval). Always justify your choice in the exam.

Question 2

What is the difference between a one-tailed and a two-tailed test?

Accepted Answer

A one-tailed test (directional) is used when you predict the direction of the difference (e.g., 'Group A will score higher than Group B'). A two-tailed test (non-directional) is used when you predict a difference but not the direction (e.g., 'There will be a difference between Group A and Group B'). The critical value for a one-tailed test is lower, making it easier to achieve significance, but you must have a strong theoretical reason for using it. In the exam, always state whether your hypothesis is directional or non-directional and choose the corresponding test.

Question 3

How do I calculate standard deviation for my Psychology NEA?

Accepted Answer

Standard deviation measures the spread of scores around the mean. To calculate it: 1) Find the mean of your data. 2) Subtract the mean from each score and square the result. 3) Sum all squared differences. 4) Divide by the number of scores (for population SD) or by n-1 (for sample SD). 5) Take the square root. For example, if scores are 4, 6, 8, mean = 6, squared differences = (4-6)^2=4, (6-6)^2=0, (8-6)^2=4, sum=8, divide by 3 (n) = 2.67, square root = 1.63. Show all steps to gain method marks.

Question 4

What does p < 0.05 actually mean in Psychology?

Accepted Answer

p < 0.05 means that there is less than a 5% probability that the observed results are due to chance alone. In other words, we are 95% confident that the results reflect a real effect. If p is less than 0.05, we reject the null hypothesis and accept the alternative hypothesis. However, this does not mean the effect is large or important; it only indicates statistical significance. Always interpret the result in the context of the study.

Question 5

Can I use a bar chart for continuous data?

Accepted Answer

No, bar charts are for discrete or categorical data (e.g., types of therapy). For continuous data (e.g., reaction times in milliseconds), you should use a histogram (no gaps between bars) or a frequency polygon. Using the wrong graph can lose marks. Always label your axes with units and include a title.

Question 6

What is a Type I error and how can I avoid it?

Accepted Answer

A Type I error occurs when you reject the null hypothesis when it is actually true (a false positive). This can happen if your significance level is too lenient (e.g., p < 0.10). To avoid it, use a stricter significance level like p < 0.05 or p < 0.01. Also, ensure your test is appropriate for your data and that you have not conducted multiple tests without correction (e.g., Bonferroni). In the exam, mention that a Type I error is more likely with a one-tailed test if the direction is wrong.

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