errors

• -          type 2 error is more likely to occur when significant levels are too strict for example when using a significance level of 0.01.
• -          Type 2 error occurs when a null hypothesis is accepted When it shouldn't have been, and this is a false negative as you're saying there is no significant difference when there actually was
• -          type one error is likely to occur when an insufficiently stringent significant level is used – so its not hard enough like p= 0.10 which is why it is unusual to sue the 10% significant level as the risk of making a type 1 error is too great – 5% is the minimum significant level used in psychology
• -          type one error is caused by optimistic research
• -          type one error occurs when the null hypothesis is rejected when it shouldn’t have been which is because the researcher has claimed to have found significant difference when there is not one so this is a false positive since the researcher made an error is accepting their experimental hypothesis
• -          when suing the 5% significance level it is up to 5% possible the hypothesis being accepted is wrong
• -          using statistical tests gives us the likelihood that our results are due to the independent variable but its possible errors can be made when deciding whether to accept or reject the null hypotheses