There are two types of errors in testing of hypothesis: Type I & Type II. Which error is more dangerous? Discuss with examples. (Refer Chapter- 14? Module-12) Discussion

There are two types of errors in testing of hypothesis: Type I & Type II. Which is the most dangerous error? Use examples to discuss. (Refer Chapter- 14? Module-12)
Discuss
CONTENT
The two most common mistakes that statisticians could make in testing hypothesis h0, (Ho), are Types I & II. One must accept the null hypothesis until there is evidence against it (Freiman, et al. 2019, 2019). Any hypothesis that is not tested must be rejected before the hypothesis can become true. If we reject the null hypothesis even though they are correct, it’s a Type I error. False reports are like a fire drill which goes off even though there’s no fire. False null rejection is considered a Type 2 error by Freiman & Associates, 2019. This is similar to a false positive that does not go off in the event of a fire. Sometimes, Type I errors can have worse consequences than Type II errors. Another context might suggest that the Type II error is better than the costly Type I error. In some cases, the answer to error can be “neither”, as Smith (2021) pointed out in Significance several years ago in regard to Six Sigma, quality enhancement, and other issues. Cont…
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