what is a type ii error 
a type ii error is a statistical term used within the context of hypothesis testing that describes the error that occurs when one fails to reject a null hypothesis that is actually false a type ii error produces a false negative also known as an error of omission for example a test for a disease may report a negative result when the patient is infected this is a type ii error because we accept the conclusion of the test as negative even though it is incorrect a type ii error can be contrasted with a type i error where researchers incorrectly reject a true null hypothesis a type ii error happens when one fails to reject a null hypothesis that is actually false a type i error produces a false positive understanding a type ii errora type ii error also known as an error of the second kind or a beta error confirms an idea that should have been rejected for instance claiming that two observances are the same despite them being different a type ii error does not reject the null hypothesis even though the alternative hypothesis is actually correct in other words a false finding is accepted as true the likelihood of a type ii error can be reduced by making more stringent criteria for rejecting a null hypothesis h0 for example if an analyst is considering anything that falls within the bounds of a 95 confidence interval as statistically insignificant a negative result then by decreasing that tolerance to 90 and subsequently narrowing the bounds you will get fewer negative results and thus reduce the chances of a false negative taking these steps however tends to increase the chances of encountering a type i error a false positive result when conducting a hypothesis test the probability or risk of making a type i error or type ii error should be considered the steps taken to reduce the chances of encountering a type ii error tend to increase the probability of a type i error type i errors vs type ii errorsthe difference between a type ii error and a type i error is that a type i error rejects the null hypothesis when it is true i e a false positive the probability of committing a type i error is equal to the level of significance that was set for the hypothesis test therefore if the level of significance is 0 05 there is a 5 chance that a type i error may occur the probability of committing a type ii error is equal to one minus the power of the test also known as beta the power of the test could be increased by increasing the sample size which decreases the risk of committing a type ii error some statistical literature will include overall significance level and type ii error risk as part of the report s analysis for example a 2021 meta analysis of exosome in the treatment of spinal cord injury recorded an overall significance level of 0 05 and a type ii error risk of 0 1 1example of a type ii errorassume a biotechnology company wants to compare how effective two of its drugs are for treating diabetes the null hypothesis states the two medications are equally effective a null hypothesis h0 is the claim that the company hopes to reject using the one tailed test the alternative hypothesis ha states that the two drugs are not equally effective the alternative hypothesis ha is the state of nature that is supported by rejecting the null hypothesis the biotech company implements a large clinical trial of 3 000 patients with diabetes to compare the treatments the company randomly divides the 3 000 patients into two equally sized groups giving one group one of the treatments and the other group the other treatment it selects a significance level of 0 05 which indicates it is willing to accept a 5 chance it may reject the null hypothesis when it is true or a 5 chance of committing a type i error assume the beta is calculated to be 0 025 or 2 5 therefore the probability of committing a type ii error is 97 5 if the two medications are not equal the null hypothesis should be rejected however if the biotech company does not reject the null hypothesis when the drugs are not equally effective then a type ii error occurs 