Inferential testing Flashcards
What is the sign test?
The sign test is a non-parametric statistical test of difference that allows a researcher to determine the significance of their investigation. It is used in studies that have used a repeated measures design, where the data collected is nominal.
How do you work out the sign test?
- To calculate the observed value add up the number of tallies for each option (+= -= ) and ignore scores of participants who have not selected one of the options (i.e. those who selected neither)
- Add up the number of times the less frequent sign occurs (e.g. identify which column has the fewest tallies) the total of the less frequent sign is the observed value (s)
- Next, you need to get the critical value from the critical value table (this will be provided in the exam). To get the critical value from the table you will need the value of N (the number of participants (omitting any participants responding ‘neither’ or ‘not with a ‘+’ or ‘-‘. In order to get the critical value from the table you will also need to know; the hypothesis type (one/two-tailed) and the probability value.
- In order for the ‘s’ value to be significant, you want the observed value (s) to be lower than the critical value (from the table). Remember the important ‘r’ rule: When there is an ‘r’ in the name of the inferential test (e.g. spearman’s rho) you want the observed value to be greater than the critical value (from the table).
What level of significance do psychologists tend to use and what does it mean?
Usually, in psychology, this will be 5% (or 0.05) as this is generally thought to be acceptable. This means that having done the statistical test, there is only a 5% (or less) probability that the results occurred due to chance factors, so the result is highly likely to have happened due to the IV- it is therefore a statistically significant result.
What is the 0.05 level of significance?
0.05 or 5% is the significance level generally used in psychology. This means that there is a 95% probability that the results of the experiment are significant, or are not significant, following the result of the statistical test. A researcher could never be 100% sure of this, as this would involve testing every single member of the population in every possible circumstance. 95% probability is seen as an acceptable level for most psychological research.
What is the 0.01 level of significance?
In some cases, if researching a sensitive topic, researchers may use a more ‘strict’ level of measurement (for example, 1% or 0.01), to be even more sure that the results are significant. This may be used on drug trials, for instance.
To work out the critical value what do researchers need to know?
- Whether a one-tailed (directional) or two-tailed (non-directional) hypothesis is being used.
- The number of participants in the study (N)- for some tests ‘degrees of freedom’ (df) is used instead.
- The level of significance- which will be (unless stated otherwise) 0.05 or 5%.
What is the observed level and the critical value?
In a statistical test, the calculation is done, and the result of the test is known as the calculated (or observed) value. This is then compared with a table of critical values. The calculated value must be greater than or lower than (depending on the test) the critical value for the result to be significant. To find out what critical value is to be used, the researcher needs to know the probability level, the number of participants, and whether the hypothesis was one-tailed (directional) or two-tailed (non-directional).
What is a type 1 error?
The alternative/experimental hypothesis is mistakenly accepted, so the null is mistakenly rejected. Therefore, the researcher says that there is a significant difference between the groups, but in reality, there isn’t (the null hypothesis should have been accepted). The chance of this in psychological research is usually 5%, due to the conventional significance level of 0.05. Type I errors are more likely when the significance level is too lenient- for example, 10% (0.1).
What is a type 11 error?
The null hypothesis is mistakenly accepted, so the alternative/experimental is mistakenly rejected. Therefore, the researcher says that there is not a significant difference between the groups, but in reality, there is (the alternative hypothesis should have been accepted). Type !! errors are more likely when the significance level is too strict- for example, 1% (0.01). A 5% level is a good balance of the risk of making a Type I or Type II error.
What are all the statistical tests?
- Sign test
- Spearman’s rho
- Pearson’s r
- Wilcoxon
- Mann-Whitney
- Related t-test
- Unrelated t-test
- Chi squared
What are the two types of statistical tests?
- Parametric
- Non-parametric
What are the parametric tests?
- Pearsons
- Unrelated t-test
- T-test
What are the non-parametric tests?
- Chi-squared
- Sign test
- Mann-Whitney
- Wilcoxon
What are tests of correlation?
- Spearmans rho
- Pearsons
What is a test of association?
Chi - squared
