| Descriptive statistics meaning (part of results) | - Measures of central tendencies |
| Descriptive statistics (maths involved) | - Averages (mean, mode, median)
- Charts and graphs
- How spread out the scores are - dispersion (range) |
| Calculating dispersion | - Standard deviation = variance squared |
| Inferential statistics meaning | - The probability that the result is significant compared to chance |
| The 5 tests | - Sign test (Binomial)
- Chi squared (x2)
- Wilcoxon test
- Mann-Whitney 'U' test
- Spearman's Rho |
| Types of data | - Nominal (tally, category)
- Ordinal (rankable, interval - time, weight) |
| Nominal data | - Sign test
- Repeated measures + matched pairs
- Chi squared
- Independent measures |
| Ordinal data | - Wilcoxon
- Repeated measures + matched pairs
- Mann-Whitney U
- Independent measures
- Spearman's Rho
- Correlation |
| Formula for Mann-Whitney's U test | U = RA - (nA (n + 1)/2) |
| What is the Table Of Critical Values | - Used to see if the U score is significant or not
- Whether it agrees with the null hypothesis or not |
| Type 1 Error | - When you reject the null hypothesis when it should have been accepted |
| Type 2 Error | - When you accept the null hypothesis when it should have been rejected |
| Statement of significance (example) | P < 0.05 (two-tailed)
P = the probability of the results being due to chance |
| Quantative data | - Information about quantities therefore numbers |
| Quantative data strengths + weaknesses | - Easy statistical analysis of difference
- Data does not tell us why there is a difference |
| Qualitive data | - Descriptive
- Observed but not measured |
| Qualitive data strengths + weaknesses | - Increases validity
- More time-consuming
- Difficult to draw comparisons |
| Demand characteristics | - The situation where the results of an experiment are biased
- When participants beliefs influence the outcome |
| How to minimise demand characteristics | - The blind technique - using deception
- Have more than one experimenter |
