1. HL Psychology Internal Assessment
Inferential Statistics

2. What you should know after this PowerPoint:
A concise review of descriptive statistics
Differences between descriptive and inferential statistics.
Why we use inferential statistics in psychology
How to properly choose an inferential statistics test.
How to distinguish between various types of data.
How to test for statistical significance.

3. Descriptive statistics provide for..
Measure of central tendency
Gives a typical value for the data set
Tells you where the middle of the data set is
Measure of dispersion
Indicates how the data are spread out
Tells you what the rest of the data are

4. Descriptive Statistics
The aim of descriptive statistics is to give an accurate summary of the data
The wrong choice of statistic gives a distorted picture of the data
This can lead to the wrong conclusions being drawn from the data
Each measure of CT and D has its advantages and disadvantages

5. Measures of Central Tendency
The mean – total scores divided by the number of scores
Adv: it uses all the values in the set, so is most sensitive to variations in the data
Dis: it can be artificially raised or lowered by an extreme value, or by skewed data
Use it when the data are normally distributed, unskewed and there are no outliers

6. Measures of Central Tendency
The median – the middle score in a range
What is the median 2,3,3,4,4,4,4,5,5,6,42?
Adv: it is based on the order of the data, not their actual values, so not distorted by extreme values
Dis: however, this makes it less sensitive to variations in the data
Use it when you can’t use the mean because of skew, outliers etc.

7. Measures of Central Tendency
The mode -most frequently occurring value
Adv: it’s the only measure suitable for summarising category/frequency data
Dis: for many data sets there is no modal value, or their may be several
Use when dealing with frequency data, and/or where there is a clear modal value in the set

8. Calculate….
A psychologist has obtained the following scores. Answer the questions below.
The range of these scores is __________________________
The mean of these scores is __________________________
The mode of these scores is __________________________
The median is ______________________________________

9. Measures of dispersion
Range-difference between the smallest and largest value Ex 3,4,7,7,8,9,12,4,17,17,18 =18-3 =16
Although quick and easy to calculate it is distorted by extreme values

10. Standard Deviation
Standard deviation – a measure of the spread of scores around the mean
It is the most sensitive measure of dispersion using all available data. It can be used to relate the sample data to the population’s parameters.

11. SD formula
Sum of all participant scores divided by the no of participants = mean
Subtract the mean from each score
Square each of these scores
Total the squared scores
Divide by one less than the total participants. This is the variance
Take the square root of the variance.

12. Work out the SD….
Scores – 13,6,10,15,10,15,5,9,10,13,6,11,7

13. Graphs
Bar chart –Shows data for categories that the researcher is interested in comparing

14. Histogram
Shows data for all categories even those with zero value

15. Frequency polygon/line graph
Shows two sets of data on one graph

16. Pie charts
Show the proportion of all scores gained by various categories

17. Inferential Statistics
HL IA ONLY

18. Inferential Statistics
With inferential statistics, you are trying to reach conclusions that extend beyond the immediate data alone. For instance, we use inferential statistics to try to infer from the sample data what the population might think.
Or, we use inferential statistics to make judgments of the probability that an observed difference between groups is a significant one or one that might have happened by chance in this study.

19. Inferential Statistics
Thus, we use inferential statistics to make inferences from our data to more general conditions; we use descriptive statistics simply to describe what's going on in our data.

20. What you are bring asked to do (HL IA).
An appropriate inferential statistical test has been chosen and explicitly justified. Results of the inferential test is accurately stated.
The null hypothesis has been accepted or rejected according to the results of the statistical test. A statement of statistical significance is appropriate and clear.

21. What you are bring asked to do (HL IA).
The information you have obtained from participants takes the form of raw data. This should go into the appendices, and you should use your results to calculate descriptive statistics appropriate to your to data.
The test you choose is dependent on the level of measurement of your data and whether you used independent samples or repeated measures.

22. Levels of Measurement
Nominal-frequency headcount; things can only belong to one category ex the no of students wearing yellow shirts.
Ordinal –data which is ranked or put in order. It is not known what the interval between each rank is ex 1st,2nd,3rd time in a swimming trial
Interval/ratio- measurement on a scale where the intervals are known and equal (ratio has a true zero point; interval can move into negs. Ex of ratio is time in secs.

23. Levels of data: nominal
Which newspaper paper do you read regularly?
We can put these into categories.

24. Levels of Data: ordinal
What grade did you get for each of your portfolio?
These can be put in order… highest to lowest

25. Levels of data: interval
How quick is your reaction time?
We can measure and compare the exact time because the intervals on the ruler are equal.

27. Inferential tests
Provide a calculated value based on the results of the investigation
This value is then compared to a critical value (statistical tables) to determine if the results are significant
In chi square, sign test, spearman’s rho the calculated value must exceed the critical value.

29. Choosing an inferential test
Nominal data and independent measures design = Chi square test
Ordinal data and independent measures design = Mann Whitney U
Interval and ratio data and independent measures design = Unrelated T-test
Nominal data and repeated measures design =Sign test
Ordinal data and repeated measures design = Wilcoxon test
Interval or ratio data and repeated measures design = related T-test
More info:

30. A directional hypothesis
Very often, we state before we test the hypothesis in which direction of the results will fall. Our hypothesis is usually directional (meaning we are predicting an increase or decrease in a time or score)and the appropriate statistical test of the hypothesis is called one-tailed.
Once you have collected the data. Decide which test you need to administer. Only one person in your group needs to work out the mathematics.

31. Using Tests of Significance – The General procedure
Choose appropriate statistical test
Calculate statistical test
Compare the test with the critical values. These can be found in the back of the Research methods text book, or mathematics statistic books, or online.
Decide which side of the critical value your result is on.
Report the decision.

32. Inferential statistics- indicating how significant results are.
A significant result is one where there is a low probability that chance factors were responsible for observed difference
5% level of significance, in psychology, is acceptable (P is less than 0.05)
There is less than a 5 % likelihood that the difference was due to chance.

33. Key Terms you will need to look up and define.
Critical value Degrees of freedom P value/level Significance One-Tailed Test Two-Tailed Test Type 1 error Type 2 error Interval Ordinal Nominal