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Introduction to statistical testing: Sign Test Inferential statistics We can infer that our results can be generalised to the target population, and as a consequence is significant. For example we may do a study and find on average that the men in a study had better spatial ability than the female subjects, but does that mean that all men around the world have superior spatial ability? Can we simply generalise the results from say 1000 participants to all males and females globally? To see if there is a real difference between the genders, we use statistical tests to test whether our results from this study could have been due to chance, or if their really does appear to be a gender difference in spatial abilities. Introduction to statistical testing Differences chance. A between two groups can be caused by statistical test is calculating the probability that chance has caused that difference. Probability recap! In psychology the significance level is set at p< 0.05, which means that there is a less than 5% probability that the results occurred by chance. In this case you are 95% certain that the difference in spatial ability is down to REAL differences between the genders (manipulation of the IV caused differences in the DV). Sign Test • Looking at a difference between two conditions • Looking at paired or related data generally repeated measures or matched pairs design • Looking at categorical/nominal data Worked Example See hand out for example… AIM: to investigate whether drinking energy drinks makes people more talkative Record the data Step 1: As the sign test uses nominal data we need to convert our data by working out which participants produced higher word count after drinking SpeedUpp and which produced a lower word count For each PP you subtract the score for water from the SpeedUpp score Record the sign, whether it is positive or negative AIM: to investigate whether drinking energy drinks makes people more talkative Step 2: Count up the number of positive signs and the number of negative signs. Step 3: The calculated S value will be the lower of these two numbers (the less frequent). S= Step 4: Find the critical value of S so we can compare it to the observed value of S. a) N= the total number of scores (ignoring any 0 or = values) b) One-tailed or two-tailed hypothesis c) p= 0.05 usual level of probability chosen (1 in 20 likelihood results are due to chance, 5%) AIM: to investigate whether drinking energy drinks makes people more talkative Step 5: compare this to our observed. the observed value/ calculated value of S ≤ critical value for the results to be significant For the Sign test, (For the data to be significant (and the alternative hypothesis to be accepted) the calculated value of S must be EQUAL to or LESS THAN the critical value identified) The results are NOT SIGNIFICANT as the calculated value of S=7 is greater than the critical value=5 (p≤ 0.05) for a two tailed test, N=20. Therefore the NULL hypothesis has to be accepted and the experimental hypothesis rejected. Practice will make perfect! Additional questions: Complete the exam question on Pg 71 in OBB – although this is an independent measures, treat it as repeated just so you can practice the sign test GHG has this example as well as pg 206-209 in GHG