Type 1 and Type 2 Errors

• Type 1 error - The researcher has used a lenient p value. The researcher thinks the results are significant when they are actually due to chance/error. (1) So they wrongly accept the alternative hypothesis and wrongly reject the null hypothesis. (1)
• Type 2 error - The researcher has used a stringent p value. They think that their results are not significant/due to chance or error when they could be significant. (1) They wrongly accept the null hypothesis and wrongly reject the alternative hypothesis. (1)
• Psychologists use the 5% significance level as it strikes a balance between the risk of making the type 1 and 2 error. (1) It is a conventional significance level. (1)
• To check for a type 1 error:

  • Compare the calculated value to a critical value from a more stringent p value. If the results are still significant then the researcher has not made a type 1 error. If the results are now not significant, then there is a chance of a type 1 error.
• A type 1 error has/has not been made. At a more stringent p value of ___, the calculated value of ___ is (more/less than) or (no longer more/less than) the critical value of ___. The results are still/now not significant. Therefore, the researcher has wrongly/correctly accepted the alternative hypothesis and wrongly/correctly rejected the null hypothesis.
• To check for a type 2 error:

  • Compare the calculated value to a critical value from a more lenient p value. If the results are still not significant then the researcher has not made a type 2 error. If the results are now significant, then there is a chance of a type 2 error.
• A type 2 error has/has not been made. At a more lenient p value of ___, the calculated value of ___ is (more/less than) or (no longer more/less than) the critical value of ___. The results are still/now not significant. Therefore, the researcher has wrongly/correctly accepted the null hypothesis and wrongly/correctly rejected the alternative hypothesis.