Scientific working - Type I and II errors

What is a type I error?

False positive (we assume our findings show smt when they don't)

p = 0.10

What could making a type I error result in?

- Rejecting the null hypothesis when it is true

- Accepting the alternatives hypothesis when it is false

- Assuming the results are due to IV when they were due to chance

How do we make a type I error?

When the p value is too high e.g. p = 0.1 (allows for chance to impact too much)

What a type II error?

False negative (we assume our findings don't show anything when they do)

p = 0.01

What could making a type II error result in?

- Accepting a null hypothesis that is false

- Wrongly rejecting an alternative hypothesis

- Assuming the results were due to chance when they were due to the IV

How do we make a type II error?

When we set the p value too low e.g. p = 0.01

Why might we use p = 0.01?

It can be necessary for testing new medical treatments such as drugs. This is because you have to be very confident when it comes to drug trials that the drug does/doesn't work

What does p = 0.05 mean?

Only 5% probability results are due to chance. 95% of the data proves that the IV affects the DV

Why do we use p = 0.05 in psychology?

To avoid type I and II errors (because it is in the middle of these errors so reduces the chance of making these mistakes)

Why do we not want to make type I or II errors?

We want to ensure we correctly accept or reject our hypotheses