Even if a treatment has an effect, it is still possible to obtain a sample mean after the treatment that is very similar to the original population mean. What outcome is likely if this happens? Group of answer choices Reject H0 and make a Type I error Correctly reject H0 Fail to reject H0 and make a Type II error Correctly fail to reject H0

Step-by-step explanation:

Hello!

Suppose that the objective of the experiment is to test if a certain treatment modifies the mean of the population of interest.

If for example, the treatment is "new fertilizer" and the population of interest is "yield of wheat crops"

Then you'd expect that using the new fertilizer will at least modify the average yield of the wheat crops.

The hypotheses will be then

H₀: μ = μ₀

H₁: μ ≠ μ₀

Where μ₀ represents the known average yield of wheat crops. (is a value, for this exercise purpose there is no need to know it)

We know that the treatment modifies the population mean, i.e. the null hypothesis is false.

The sample we took to test whether or nor the new fertilizer works conducts us to believe, it does not affect, in other words, we fail to reject the null hypothesis.

Then we are in a situation where we failed to reject a false null hypothesis, this situation is known as Type II error.

I hope this helps!

idk

step-by-step explanation:

b) 1/3 * 1/5

step-by-step explanation:

each smallest part is 1/15 since the written one is 1/5 and it's divided by 3 equal parts. (1/5 )/3 = 1/15 = 1/3 * 1/5