Inferential Statistics

Why are they used? 1)Statistical tests are used by Psychologists(and maths boffins) to prove or disprove a Hypothesis. 2) Statistical tests are used to determine whether data shows significance(if there is a relation or effect in the experiment). 3) Statistical tests see if the NULL Hypothesis is true(that results are due to chance/random). So the test is there to find out if the result was found by chance.

In fact we want to be 95% sure our results were not due to chance In fact we want to be 95% sure our results were not due to chance. So we do a statistical test to find out. What does p ≤ 0.05 level of significance mean? This means that this is a 95% probability that the change in the DV is as a result of the IV and a less than 5% probability that this is due to random chance. The findings are therefore statistically significant. Thus we can reject the null hypothesis and accept the experimental/alternative hypothesis.

What You Need to Know for The Exam…. Know the purpose of using Inferential Statistics. Know what test you would use and why. Know how to conduct the test. Whether or not the results are significant?

Choosing the Correct Statistical Test To decide what test you will use you need the following information: Difference/Correlation/Association: (This you will gather from the information given in the statement provided to you on the test. Type of Participant Design: Independent Measures, Matched Pairs, Repeated Measures Type of Data: NOI They may refer to the terms parametric and non parametric on your exam.

Difference/Correlation: Hypothesis Hypothesis: predicts the outcome of the study A one-tailed hypothesis (directional) predicts the expected direction of results. A two-tailed hypothesis (non-directional) which does not predict the expected direction of the results. There also needs to be a null hypothesis. This hypothesis suggests that are results are down to chance and not your IV. They must be operationalised: to make the variables measurable

What type of hypothesis am I describing? 1. There will be a significant difference in test score performance, measured by % on a 10 question Maths Test between children who are born between October and April than children who are born between May and September. 2. Children who are born between October and April will perform significantly better in test score performance, measured by % on a 10 question Maths Test than children who are born between May and September. 3. There will be no significant difference in test score performance, measured by % on a 10 question Maths Test between children who are born between October and April than children who are born between May and September. Any difference will be down to chance.

How your participants are used. Type of Participant Design: Independent Measures, Matched Pairs, Repeated Measures How your participants are used.

Which One is Which Bandura, Ross and Ross (1961) tested 74 preschoolers. They were put into groups: a control group, single sex groups who saw a violent model of either same sex or a different sex and single-sex groups who saw a non-violent model of either the same sex or a different sex. Children who saw a model who was violent were more likely to prelicate violent acts, especially if the model was the same sex as them A researcher wanted to test the effectiveness of two different treatments for Schizophrenia. They divided the participants into two groups and each group received one treatment. The participants were allocated according to age, gender and how long they had has the disorder. Kiecolt-Glaser (1984) took blood samples from 75 first year medical students a) one month before their final examinations (relatively low stress), and (b) during the examinations (high stress). She found that they had reduced immune systems during times of stress.

Type of Data: NOI Nominal: categories Ordinal: ranking Interval: data measure using units

A set of surgical records classifies patients as ‘chronic’, ‘acute’, or ‘not yet classified.’ What level of measurement is being used? The students organised themselves in order of what month of the year that they were born. They started with January and ended with December. Students wrote a test and the results were presented in %.

Chi Squared or (x ²) Difference or an association between two variables Independent Measures Nominal data

Underline: the Design, The Nominal data and how we know that it is a test of difference. 156 boys and 204 girls were asked whether they liked vanilla slices. Is there any evidence that there is a sex difference in preference for vanilla slices? Boys Girls Total Like 94 175 269 Did not like 62 29 91 156 204 360

Step 1: Label the cells (excluding totals) Boys Girls Total Like 94 175 269 Did not like 62 29 91 156 204 360

E= Total of Row x Total of Column Total Step 2: Work out the E values for each cell E= Total of Row x Total of Column Total Boys Girls Total Like 94 a 175 b 269 Did not like 62 c 29 d 91 156 204 360 Cell Working E value A (269 x 156)÷ 360 B C D

X2= (Total of Cell O – E)2 E Step 3: Work out X2 Value (Chi square) Working E value A (269 x 156) ÷ 360 116.57… B (269 x 204) ÷ 360 152.43… C (91x 156) ÷ 360 39.43… D (91 x 204) ÷ 360 51.57… Cell Working X2 value A (94 - 116.57) 2 ÷ 116.57 4.42 B C D

Step 4: Work out the Total X2 Value Add all the individual X2 values. Total = X2 value 4.42 3.34 12.92 9.88

Step 5: Work out the ‘c’ value df= (Total number of rows -1) x ( Total number of columns – 1) df is the number which runs along the left side of the table of critical values In this case our df= In some tables df appears as V.

Step 6: Using a table of critical values, find if the data is significant To be significant X2 value must be equal to or exceed that found in the table. Use 0.05 degree of accuracy df P = 0.05 P = 0.01 P = 0.001 1 3.84 6.64 10.83 2 5.99 9.21 13.82 3 7.82 11.35 16.27 4 9.49 13.28 18.47 5 11.07 15.09 20.52 6 12.59 16.81 22.46 7 14.07 18.48 24.32 8 15.51 20.09 26.13 9 16.92 21.67 27.88 10 18.31 23.21 29.59 11 19.68 24.73 31.26 12 21.03 26.22 32.91 13 22.36 27.69 34.53 14 23.69 29.14 36.12 15 25.00 30.58 37.70 16 26.30 32.00 39.25 17 27.59 33.41 40.79 18 28.87 34.81 42.31 19 30.14 36.19 43.82 20 31.41 37.57 45.32

Are the Results Significant? x ² (30.56) Critical Value is (3.84) What does this mean?

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