Answer :
The correct answers to your questions are:
Type I (alpha) error have occurred.
10%, which is 1 - alpha (0.10) is the chances of happening.
In hypothesis testing, there are two types of errors that can occur:
Type I Error (False Positive):
This occurs when you reject a true null hypothesis. In other words, you conclude that there is a significant effect when there isn't one in reality. The probability of making a Type I error is denoted by the symbol "alpha" (α). In your question, you're testing at a 10% alpha level, so the probability of Type I error is 0.10.
Type II Error (False Negative):
This occurs when you fail to reject a false null hypothesis. In other words, you conclude that there isn't a significant effect when there actually is one. The probability of making a Type II error is denoted by the symbol "beta" (β).
Now, let's apply this to your scenario. You want to test if the true average temperature of COVID patients exceeds 100 degrees, given a sample of 56 patients with a known population standard deviation of 6. You're testing at a 10% alpha level.
Null Hypothesis (H₀): The true average temperature is not greater than 100 degrees.
Alternative Hypothesis (H₁): The true average temperature is greater than 100 degrees.
If you reject the null hypothesis when it's actually true, that would be a Type I error. So, the answer is "alpha" (Type I) error.
The chances of making a Type I error are directly related to the chosen significance level (alpha level) of your test. In your case, you're testing at a 10% alpha level, so the chances of making a Type I error are 10%, which is equal to 1 - alpha.
So, the correct answers to your questions are:
Type I (alpha) error.
10%, which is 1 - alpha (0.10).
Learn more about hypothesis testing click;
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