Question

Explain hypothesis testing for population variance, how is it different from other hypothesis tests? What is a real life example following the hypothesis testing procedure.

Answer #1

The rejection rule states that, the null hypothesis is rejected
if *p*-value is less than the level of significance and the
null hypothesis is not rejected if the *p*-value is greater
than the level of significance.

The p-value associated with out test statistic is smaller than the significance level of the test we have chosen.

The null hypothesis is rejected when the *p*-value is
less than the given level of significance.

The experiment is conducted to test whether there is a relationship between the type of verdict and the type of abuse experienced by the defendant.

State the hypotheses.

Null hypothesis:

There is no relationship between the type of verdict and the type of abuse.

Alternative hypothesis:

There is relationship between the type of verdict and the type of abuse.

From that the given information, the researcher failed to reject the null hypothesis. This implies that there is no relationship between the type of verdict and the type of abuse'

There is no relationship between verdicts and type of abuse.

The test is conducted to determine whether there is a relationship between the type of verdict and the type of abuse. The researcher had failed to reject the null hypothesis. Hence, it can be concluded that no relationship exists between verdicts and type of abuse.

The probability of getting the value of the statistic that is as
extreme as the observed statistic considering the null hypothesis
is true is called as *p*-value.

The probability with which a test statistic would occur if the null hypothesis were true

When the null hypothesis is assumed to be true the probability
value for which the test statistic would occur as extreme as the
observed statistic is termed as *p*-value.

if there is any relationship between "point estimate", "the
standart deviation", "the hypothesis testing", explain on a page,
and give a real-life example. (Please it must be in the form of an
article.)

explain what is meant by a p-value and how it is used in hypothesis
tests flr fhe population mwan

Hypothesis testing is the basis of inferential statistics.
Statisticians are always coming up with new tests and testing new
characteristics of population parameters. One of the simplest tests
that currently exist is the one-sample test for means.
A random sample is drawn. If the population variance is known,
then we use the Z test; if the population variance is unknown, we
use the T test. In addition, there are some additional assumptions
that can be made. For example, if a...

Locate an example of a research study that uses hypothesis
testing. Explain whether the study describes its hypothesis testing
procedure explicitly or implicitly, based on the explanations in
the Methodology section. Finally, discuss what this statistical
technique allowed the researchers to accomplish and/or conclude in
the study.

explain how hypothesis testing is not foolproof.

an example scenario of how organizations use difference in
population means in hypothesis testing before making decisions

What are the steps in hypothesis testing?
What is the goal of hypothesis testing?
What are null and alternative hypotheses?
In §9.2 the concepts of Type I and Type II errors are
introduced.Consider the situation where a husband and wife go to
the doctor’s office to each get some tests run and the doctor
accidentally mixes up their charts. The doctor comes into the
exam room with the results of the tests and declares that the wife
is NOT pregnant but...

1. We are interested in testing whether the variance of a
population is significantly less than 1.44. The null hypothesis for
this test is
a. Ho: s2 < 1.44
b. Ho: s2 >= 1.44
2. How do I do this by the calculator?
The 95% confidence interval estimate for a population variance
when a sample variance of 30 is obtained from a sample of 12 items
is: answer is 15.05474 to 86.38743

Take a moment to reflect on what you have learned in this module
about hypothesis testing in general and A/B testing in particular.
In what other scenarios or industries do you think this type of
analysis would be helpful? What precautions should you take when
designing such tests? How can you ensure that your results are
representative of your target population?

This question extends the hypothesis testing to analyze
difference between population proportions based on 2 or more
samples, and to test the hypothesis of independence in the joint
responses to 2 categorical variables. Can we provide a real world
example for using the Chi-square test along with expectation of the
outcomes?

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