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Can you explain the risks of committing a type 1 error in the FDA?
Wiki User
∙ 11y ago
Lukenge Matthew,
lukenmat@gmail.com
UVRI-Uganda.
Type 1 error is a null rejection error (wrongfully rejecting the null which states that there will be no difference in anticipated observations in the study groups). Type II error, however, is the null acceptance error. The consequences of committing a type I error are way more grave compared to type II.
The FDA caters for food and drug regulation meant for human consumption. An example of a null hypothesis is Drug X has no cancerous impact in humans while the alternative will be Drug X has cancerous impact on humans.
Usually the critical value is set at 0.05% but in drugs its at 0.01%, i.e. the researcher has 1% chance of committing type I error. if one performs the statistical tests and comes up with say a Z-score of (0.09) less than 1.65 (0.05%) he/ she will reject the null but assuming this 0.09 was a wrong value supposed to be 2.29 thus supposedly meant to accept the null and therefore not recommending the use of the cancerous drug.
Now, if one wrongly rejected the null, the inferance will be that the drug is good and not cancerous, therefore, this researcher will full blown expose the public to a cancerous drug, however, if he committed a type II where a drug which isnt actually cancerous is rejected on a wrong statistical finding that its cancerous, no one will use the fine drug and probably the pharmacueticals will wrongly loose out but the impact isnt as grave as it would be if a cancerous drug is wrongfully allowed on the market by commiting the type 1 error.
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∙ 11y ago
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Relationship between type 1 error and type 2 error?
In statistics, there are two types of errors for hypothesis tests: Type 1 error and Type 2 error. Type 1 error is when the null hypothesis is rejected, but actually true. It is often called alpha. An example of Type 1 error would be a "false positive" for a disease. Type 2 error is when the null hypothesis is not rejected, but actually false. It is often called beta. An example of Type 2 error would be a "false negative" for a disease. Type 1 error and Type 2 error have an inverse relationship. The larger the Type 1 error is, the smaller the Type 2 error is. The smaller the Type 2 error is, the larger the Type 2 error is. Type 1 error and Type 2 error both can be reduced if the sample size is increased.
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