Type I and Type II Errors

Question 1

what is a type I error?

Answer 1

when the null hypothesis is rejected and the alternative hypothesis may be accepted (should be the other way round, as in reality the null hypothesis is true)
- referred as an optimistic error or false positive as the researcher claims to have found a significant difference or correlation that does not exist

Question 2

what is a type II error?

Answer 2

when the null hypothesis is accepted but it should be alternative hypothesis as in reality is true
- known as a pessimistic error or false negative

Question 3

what is the percent that the wrong hypothesis is accepted?

Answer 3

5%

Question 4

when are we more likely to make a type I error?

Answer 4

if significance level is too lenient, too high
- e.g 10% instead of 5%`

Question 5

when are we more likely to make a type II error?

Answer 5

if significance level is too stringent, too low
- e.g. 1% instead of 5%