✓ Inferential Statistics - significance testing (AO1)

Question 1

What are the 4 steps to significance testing?

Answer 1

Probability
Significance
Calculated Value
Critical Value

Question 2

What is probability?

Answer 2

“the likelihood a certain outcome will happen”

Question 3

How is probability used in psychology?

Answer 3

• look at the likelihood our IV caused the DV (or that there is a link between 2 variables)
• at the start of the experiment you write experimental AND null hypothesis
• if there is a high probability of our results being due to chance we accept the null hypothesis & reject the experimental hypothesis
• if there is a low probability “ vice versa

Question 4

What is signficance?

Answer 4

in psych we represent probability with a “significance level”

Question 5

How is significance used in psychology?

Answer 5

• the normal level used is 5% (probability of the result being due to chance/other extraneous variables is less than 5%)
• written as p <_ 0.05
• this means the researcher is 95% sure the results are due to the IV causing the DV/or the 2 variables are linked
• sometimes we use the p<_0.01 (1% chance & 99% certain)
• OR p <_0.10 (10% & 90%)

Question 6

What is calculated value?

Answer 6

after completing the statistical test you will get a “calculated value”
(e.g. for the sign test it could be s=3)
- this number needs to be compared to the critical value

Question 7

What is critical value?

Answer 7

the number in the statistical table that needs to be compared to the calculated value from the statistical test
- to find the correct critical value you need the 3 pieces of information

Question 8

What are the 3 pieces of information you need to find the correct critical value?

Answer 8

number of ppts:
- this is referred to as “N”
(for a chi-squared test “Df” is given instead)

hypothesis table:
- directional = one-tailed
- non-directional = two-tailed

level of significance:
- usually p=0.05
- sometimes p=0.10/0.01

Question 9

What are potential errors?

Answer 9

we can never be 100% sure of an outcome because there is always potential for error to occur

Question 10

What is a type 1 error?

Answer 10

false positive:
- when we mistakenly accept the experimental hypothesis (thinking our results are signif.) & reject the null hypothesis
- most likely to happen with p<_0.10 which leaves 10% room for chance/error (overly lenient)

Question 11

What is a type 2 error?

Answer 11

false negative:
- when we mistakenly accept the null hypothesis (thinking our results are non-signif.) & reject the experimental hypothesis
- most likely to happen with p<_0.01 which only leaves room for 1% chance/error (overly harsh)