1. Learning Objectives: 1. Understand the use of significance levels. 2
Learning Objectives: 1.Understand the use of significance levels. 2.Know how to look up critical values to discover the result of a test.
Suppose your friend
said she could predict
the sex of any unborn
baby.
How would you put
her to the test?
How many would you
expect her to get right out
of 100 to be convinced?

2. Significance Levels
When we carry out statistical tests in Psychology we would need to be 95% confident of the outcome, leaving only 5% room for error.
This is called the 5% significance level or p=<0.05 where p is the probability that the results are due to chance.
There are others......
10% (p=<0.1) Not significant. Results due to chance.
5% (p=<0.05)Results are Significant (e.g. IV affected DV)
1% (p=<0.01) Highly Significant.
0.1% (p=<0.001) The strictest significance level.

3. The 10% level
Results are explained as probably due to CHANCE.
Pilot studies accept the 10% level as an indication that a full scale study might be worth carrying out.
The 5% level
It has become the CONVENTION to use this for most psychological studies.
The 1% level
Sometimes we need to be more certain of the results of a study: If we are about to challenge an established theory; Or if we are carrying out research affecting health such as drug testing.
In this case we are more STRICT and use the 1% level before we accept results as significant.

4. One & Two-Tailed Probability
When we predict a directional hypothesis we say we are carrying out a ONE-TAILED TEST.
When we predict a non-directional
hypothesis we say we are carrying
out a TWO-TAILED TEST.
It is important to use the correct
table when looking up
the result of our statistical test in
the tables of critical values.

5. Degrees of Freedom or N
In order to check the significance of the calculated value from your statistical test, you need to compare it with a CRITICAL VALUE. To find this you need to know the Degrees of Freedom.
In most cases, you get this by
looking at the number of
participants in your study (N).
In studies that use an
independent design there are 2
values of N (N1 & N2).
For Chi square DF is based on
how many cells you have in
your results table.

6. Comparing your Calculated (observed) value with the Critical value
So you need to know
Significance level (usually 5%)
Degrees of Freedom or N (or N1 & N2)
Whether or not you are looking at a one or two-tailed test.
Your calculated (observed) value.
Some tests require the calculated value to be equal to or greater than the critical value for it to be significant. For others it is the reverse. It will state which under the table.
TIP: It just so happens that .....if the test has an R in its name then the observed value has to be greater than the critical value.

7. Suppose your calculated value from a chi square test was 9
Suppose your calculated value from a chi square test was 9.8 ,the DF=4 and the signif level is p=<0.05. This table is for a one-tailed test. What is the critical Value? Is your observed Value significant? If yes you can accept the H1. If no then you must reject the H1 in favour of the Null hypothesis.

8. Now answer these questions:
For each of the following tests state whether or not the observed value has to be higher or lower than the critical value to be significant
Wilcoxon Test
Sign Test
Related T test
Mann-Whitney U Test
Pearson’s R test.
Spearman’s Rank (Rho) Test
2. When would you use a 1% (p=<0.01) significance level?
3. When would you use tables of critical values for a one-tailed test?

9. Type 1 and Type 2 Errors We may be right or we may be wrong!
So we have done our stats test, checked for significance at 5% and stated whether or not we have accepted or rejected our null hypothesis. Job done. But
We may be right or
we may be wrong!

10. Type 1 Error If a researcher claims support for the H1 with a significant result, when in fact the results were actually due to chance, then a type 1 error has been made. THINK: With which significance level is this most likely to happen and why? Type 2 error If a researcher fails to achieve a significant result, even though the effect they are trying to demonstrate actually does exist, then a type 2 error has been made. So, the H1 would have been wrongly rejected. THINK: Can you think of 2 reasons why this could occur?

11. TASK: Indicate below with which significance level there is the greatest risk of a Type 1 error and with which there is the greatest risk of a Type 2 error:
10% (p=<0.1) –
5% (p=<0.05) –
1% (p=<0.01) –
0.1% (p=<0.001) -