You can't compare apples and orange

The test statistic value lies to the right of the critical value. So we have sufficient evidence to reject the null hypothesis.

<h3>What are null hypotheses and alternative hypotheses?</h3>

In null hypotheses, there is no relationship between the two phenomenons under the assumption or it is not associated with the group. And in alternative hypotheses, there is a relationship between the two chosen unknowns.

Students who take a statistics course are given a pre-test on the concepts and skills for the first chapter of a statistics course.

Then they are given a post-test once the professor has concluded lecturing on the material.

Pre-test and post-test scores for 4 students in an elementary statistics class are given below.

Then we have

$\mu _d = \mu _{post} - \mu _{pre}$

Then the null hypotheses and alternative hypotheses will be

H₀: $\mu _d = 0$

Hₐ: $\mu _d > 0$

Then the test statistic will be

$\rm \overline{x} _d = \dfrac{\Sigma x_d}{n} = \dfrac{15+12+10+1}{4}\\\\\overline{x} _d = 9.5$

Then

$\rm S_d = 6.02$

The test statistic value is given by

$\rm t = \dfrac{\overline{x} _d }{\dfrac{S_d}{\sqrtn}} \\\\t = \dfrac{9.5}{\dfrac{6.02}{\sqrt4}}\\\\t = 3.16$

Since this is a right-tailed test, so the critical value is given by

$\rm t_{n-1}(\alpha ) = t_3 (0.05) = 2.353$

Since the test statistic value lies to the right of the critical value. So we have sufficient evidence to reject the null hypothesis.

Hence, we can conclude that $\mu _d > 0$ that is test scores have improved.

More about the null hypotheses and alternative hypotheses link is given below.

brainly.com/question/9504281

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2 years ago

When sampling from a population, the sample mean will: Group of answer choices always be closer to the population mean as the sa

Radda [10]

Answer:

When sampling from a population, the sample mean will: be closer to the population mean as the sample size increases.

Step-by-step explanation:

The sample mean is not always equal to the population mean but if we increase the number of samples then the mean of the sample would become more and more closer to the population mean.

Usually the population size is very huge that is why we select a random sample from the population, care must be taken to ensure randomized sampling otherwise results would not be accurate. After that we have to make sure that the number of samples are enough for the given population size. The number of samples depends upon the shape of the population. If the population is normal than according to central limit theorem, a less number of samples would be enough to ensure normal distribution of sampling mean, otherwise a greater sample size will be required.

8 0

3 years ago