Type 1 and Type 2 errors

Question 1

What is a type 1 error?

Answer 1

Refers to a situation in which we assume that our findings show something when they don’t

• rejecting a null hypothesis that is true
• wrongly accepting the alternative hypothesis
• assuming the results were due to the IV when they were in fact due to chance
  THEREFORE WE SHOULD HAVE KEPT THE NULL HYPOTHESIS THAN REJECTING IT

Question 2

What increases the likelihood of type 1 errors?

Answer 2

• if we set the p-value too leniently eg p10%
• this is because a higher p value increases the possibility that the results were due to chance

Question 3

What is a type 2 error?

Answer 3

Refers to a situation which we may miss something that is actually happening!

• accepting a null hypothesis that is false
• wrongly rejecting the alternative hypothesis
• assuming the results were due to chance when they were in fact due to the IV
  THEREFORE WE SHOULD HAVE REJECTED THE NULL HYPOTHESIS RATHER THAN ACCEPTING IT!

Question 4

What increases the likelihood of type 2 errors?

Answer 4

• More likely if the significance level is too stringent (low) e.g. more likely at 1% than at 5%