1. Lecture 2: Basic steps in SPSS and some tests of statistical inference
Error checking
Missing values analysis
Grouping variables
Graphical representation
Break
Hypothesis testing, inferential statistics and parametric testing in SPSS

2. Exercises
Exercise data can be found on the website:

3. Statistical inference: the null and alternative hypotheses (H0, H1)
Statistical inference is concerned with differences between samples / populations
Null hypothesis:
“There is no significant difference between A and B”
Alternative hypothesis:
“There is a significant difference between A and B”

4. One and Two tailed tests
Very often, we will want to examine the ‘direction’ of a difference
One-tailed test: specifying the direction
Two-tailed test: no direction specified
Alternative hypothesis changes for one-tailed test:
“A is significantly greater or less than B”

5. Determining your data types
Population: all possible cases
Sample: your selection of the population
Tied or ‘paired’ samples:
Samples that are linked, perhaps in time (e.g. before/after samples)

6. What type of test is appropriate?
Parametric:
Classical tests
Non-parametric:
Less powerful tests
Knowing your data

7. Test selection The choice of statistical test is crucial
Various tests depending on test requirements:
Parametric
Non-parametric
Test calculation can be undertaken in SPSS

8. Significance testing
How significant is the test result? Does it show a ‘real’ difference, or could it have occurred by chance?
With every test, there will be a probability distribution, which looks like the normal distribution
This distribution specifies the probability of the result (test statistic) occurring by chance or random variation

9. Significance - continued
Probability is measured as 100% = 1.00
Each test has a critical ‘rejection’ area on its distribution where test statistics must be rejected, as they are too large than to have occurred by chance, according to the number in the sample
Accordingly, all tests have critical values for different levels of significance and differently tailed tests

10. Interpreting significance
To simplify matters, we use significance ‘levels’ to determine if a test is significant
We usually use 0.05 (5%) or you could use 0.01 (1%)
This means that there is a 5% probability that the result occurred by chance, i.e. we can be 95% confident in its importance