Identify factors that influence the significance level and power of a hypothesis test. Which of the following statements is FALSE? a. Alpha (a) is equal to the probability of making a Type I error.
b. A smaller sample size would increase the effectiveness of a hypothesis test.
c. The probability of rejecting the null hypothesis when the null hypothesis is true is called a Type 1 Error.
d. The power of a hypothesis test is the probability of not making a Type Il error.

b. A smaller sample size would increase the effectiveness of a hypothesis test.

Step-by-step explanation:

A hypothesis test is an important procedure in the field of statistics. It evaluates any two mutually exclusive statements about a population that determines and tells which statement made best supports to the sample data provided.

Now we know that an increase in the sample size makes the hypothesis test more effective and sensitive and it is more likely to reject the null hypothesis.

The probability of making a type I error is known as the Alpha while the probability of making type II error is called Beta.

Thus the correct option is (b).

3< x< 5

step-by-step explanation:

|x - 4| < 1

there are two solutions, one positive and one negative.   remember to flip the inequality when taking the negative solution

x-4 < 1       x-4 > -1

add 4 to each side

x-4+4 < 1+4       x-4+4 > -1+4

x< 5                 x> 3

3< x< 5