Type 1 and Type 2 errors

Question 1

What is a type 1 error?

Answer 1

A false positive
It happens when you think there is an effect when there actually isn’t one
In simple terms: you think something worked, but it actually didn’t

Question 2

What happens to the null hypothesis in a type 1 error?

Answer 2

You reject the null hypothesis, but it was actually true

Question 3

Give an example of a type 1 error

Answer 3

EG: A researcher thinks a new revision technique improves memory scores, but the difference in results actually happened by chance

Question 4

What is a type 2 error?

Answer 4

A false negative
It happens when you think there is no effect when there actually is one
In simple terms: you miss a real effect

Question 5

What happens to the null hypothesis in a type 2 error?

Answer 5

You accept the null hypothesis. but it was actually false

Question 6

Give an example of a type 2 error

Answer 6

EG: A researcher concludes a therapy does not reduce anxiety, but it actually does work

Question 7

What are three ways to reduce type 1 errors?

Answer 7

1. Lower the significance level
   Change p<0.05 –> p<0.05
2. Replicate the study
   Repeat the research to check if the results occur again
   If it doesn’t replicate, the original result may have been a type 1 error
3. Reduce researcher bias
   Use standardised procedures, double-blind methods or objective measures
   Prevents researchers accidentally influencing the result

Question 8

What are three ways to reduce type 2 errors?

Answer 8

1. Increase the significance level slightly
   Change p<0.01 –> p<0.05
2. Increase the sample size
   Larger samples increase statistical power
   This makes it easier to detect real effects
3. Reduce measurement error
   Use reliable instruments and clear procedures