1. Learning Aims
By the end of this session you are going to totally ‘get’ levels of significance and why we do statistical tests!

2. Just a recap …. On your whiteboards.. On your own…
What is the IV?
What is the DV?
What is a directional hypothesis?
What is a non-directional hypothesis?
What is the mean?
What is the median?
What is the mode?
What are these 3 averages called collectively?

3. Levels of measurement/data - DV
These are quantitative measures of data which are of extreme importance when conducting statistical tests
There are 4 levels of measurement

4. Also known as levels of data
Nominal: Counting into categories, e.g. there are 4 men and 4 women in the room
Ordinal: Results are put in order, they are ranked. E.g. we could rank the place that each horse came in a race

5. Levels of measurement
Interval: Data is defined as being a specific measure, this can be measured on an actual instrument, there are equal intervals between each piece of data. E.g. We can record the exact temperature using a thermometer. (can be minus)
Ratio: This is like interval data except the scale has a meaningful value of zero. E.g. time and length can be classed as Interval

6. Why do we need to conduct statistical tests?
Statistical tests tell us the significance of a set of findings- did the IV really effect the DV or were the findings a fluke?
The more significant a finding is the more effect the IV had on the DV

7. Probability:
We need to use inferential statistics to tell us if the result that we have found is due to chance or not
The minimum accepted level of probability commonly used in psychology is 5%, this is represented as 0.05
If the level of significance achieved from a test is equal to or less 0.05 than the results are said to be significant
This would mean that we are 95% sure that the IV caused the change in the DV

8. Probability:
Can be expressed as:
A proportion: a 1 in 5 chance.
As a percentage: 20%
More commonly expressed as a decimal in psychology: 0.2.
In psychology: 10%=0.10, 5%=0.05, 1%=0.01 and 0.1%=0.001
To go from % to decimal divide by 100, move decimal place 2 spaces to the left.
Remember the more stringent (lower) the level of significance, the more significant the results are

9. Observed value =
Every time you perform a statistical test you end up with one number which is called the OBSERVED VALUE..also called the calculated value because you calculated it 
Raw data = chicken food Stat test = chicken
Put the data into the chicken and it produces an egg
Data>> stat test>>observed value

10. This observed value is then compared to a table of CRITICAL VALUES to see whether your results are significant or not
To be significant the observed value should be either greater or less than the critical value depending on the type of test selected
There are different tables of values for different statistical tests
There are different pages for different levels of significance

11. Interpreting results:
Usually in psychology if the results are significant it means that the probability of the result being due to chance is 5% or less
P<0.05 means the results are significant- so we would accept the experimental hypothesis and reject the null hypothesis

12. Interpreting results:
P is used to represent “the probability that the results are due to chance”
> =means greater than
< =means less than
≥ means greater than or equal to
≤ means less than or equal to
SO………………
P<0.05 means that the probability that the result is due to chance is less than 5%

13. Type 1 and type 2 errors: These can occur because:
The 5% level of significance has been accepted as it represents a reasonable balance between the chances of making a type 1 or type 2 error
These can occur because:
Level of probability accepted is either too lenient (too high) or too stringent (too low)

14. Type 1 and type 2 errors Type 1 error: Type 2 error:
Occurs when we conclude that there IS a significant difference when there is NOT
This can happen if the accepted level of probability is set TOO LENIENT
Significance level set at 20%
Type 2 error:
Occurs when we reject the experimental hypothesis and accept the null when there IS a difference
This can happen if the probability level is TOO STRINGENT
Significance level set at 1%

16. Deciding on a statistical test
You must decide the following:
Are you trying to find out if your samples are related (correlate) or if there is a difference between 2 conditions?
What experimental design was used independent measures or repeated measures?
What was the level of data? (NOIR)
You can use the following table to help

17. See handout… What test to use? Looking for a difference (experiment)
Looking for a correlation

18. What test to use? rho Looking for a difference Chi-square Sign Test
LOOKING FOR A CORRELATION
Groups will be separate
DATA LEVEL
INDEPENDENT
GROUPS DESIGN
REPEATED MEASURES DESIGN
NOMINAL
Chi-square
Sign Test
ORDINAL
Mann-Whitney U test
Wilcoxon test
Spearman’s
rho
INTERVAL OR RATIO
Unrelated t test
Related t test
Pearsons r

19. What test to use? rho Looking for a difference Chi-square Sign Test
LOOKING FOR A CORRELATION
Groups will be separate
DATA LEVEL
INDEPENDENT
GROUPS DESIGN
REPEATED MEASURES DESIGN
NOMINAL
Chi-square
Sign Test
ORDINAL
Mann-Whitney U test
Wilcoxon test
Spearman’s
rho
INTERVAL OR RATIO
Unrelated t test
Related t test
Pearsons r

20. Cats Smell Mainly When Urine’s Released
Cats Stink Pissy