What are Type Ⅰ Errors and Type Ⅱ Errors when introducing a new drug? If the level of scrutiny is too high, the cost of Type Ⅱ Errors to society will surge. If the level of scrutiny is too low, society may suffer from the high cost of Type Ⅰ Errors. What is the optimal level of scrutiny to balance the cost of Type Ⅰ Errors and Type Ⅱ Errors? Why? Will your answers be different in the case of the vaccine of common flu and the case of the vaccine of COVID-19? Explain.
Confidence intervals provide information about the likelihood of one of two main types of errors in therapeutic testing: • Type I error, ie. identifying differences between treatment methods, when in reality there are no differences; • type II error, i.e. differences between methods are not detected, when in reality they exist, and to such an extent that doctors would like to receive information about this, the expected difference. Researchers have to decide for themselves what expected difference and what degree of probability (for any type of error) they consider acceptable if the findings are used as a guide to action; statistical significance test alone cannot help. Statistical tests do not suggest that the difference is due to the fact that one method is better or not better than another, depending on the circumstances; they simply reveal the degree of probability. Distinguishing may be statistically significant and have narrow confidence intervals, but it may nevertheless be clinically insignificant.
For any disease, vaccination with an untested drug increases the risk of loss of health, whether it is the flu or COVID-19.
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