If the consequences of making a type i error are severe, I would choose 0.01 as the level of significance.
A Type I error is when the null hypothesis is rejected even though it is correct.
Many people choose a maximum p-value at which they will reject the null hypothesis before conducting a hypothesis test. This value is frequently represented by the symbol (alpha), also known as the significance level.
The outcome of a hypothesis test is referred to as statistically significant when the p-value is less than the significance level.
An extensive clinical trial is conducted, for instance, to contrast a novel medical procedure with an established one. According to the statistical study, adopting the new medication instead of the old one results in a statistically significant increase in lifespan. However, the average increase in lifespan is less than 24 hours, the increase in lifespan is just three days, and the quality of life suffers throughout the extra time. The majority of people wouldn't view the improvement as being all that remarkable.
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