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nbme15/Block 3/Question#48 (4.6 difficulty score)
A paper says, "We chose the sample size to ...
5%, Type I error🔍
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+4  upvote downvote
submitted by cassdawg(580),

In hypothesis testing, the alpha value is the probability of making a Type I error. This is the type of error mentioned in the question.

• Type I error - you find a difference when a difference doens't really exist.
• One way of remembering this is that this is what scientists "want" to make: they want to find a significant difference in their data, thus it is the "first mistake" they'd make.
• Alpha is the probability you are willing to accept that you could have made a type I error (i.e. an alpha value of 0.05 means there is a 5% probability you could make a type I error and reject the null hypothesis when you should not)

• Type II error - you do not find a difference when you should have because a true difference really exists
• Beta is the probability that you make a type II error
• Power is equal to (1 - beta)
• Power can be increased by increasing sample size, and thus with a larger sample you have a lower probability of making a Type II error
• Power can also be increased by increasing expected effect size or increasing precision. It is interesting to note accuracy has no effect on power.

FA2020 p263

cheesetouch  FA2018 p258 +