In need of a 125 response/discussion to EACH of the following forum posts. There are (3) different Forum posts. Agreement/disagreement/and/or continuing the discussion.The three interactive posts should each be substantial, relevant, and engaging. Origina

I’m working on a Business question and need guidance to help me study.

In need of a 125 response/discussion to EACH of the following forum posts. There are (3) different Forum posts. Agreement/disagreement/and/or continuing the discussion.The three interactive posts should each be substantial, relevant, and engaging. Original forums discussion/topic post is as follows: (Use/Cite references to support your ideas)

*** This posts are based off of these statements:

Before beginning this prompt, you will want to completely understand Statistical Inference as presented in Chapter 7. Then choose one of the following topics and thoroughly explain it to the class. Before choosing your topic to research and report on, check to make sure one of your fellow students has not already chosen it. Here are the topics to choose from***

FORUM POST 1: Level of significance

Hi class,

The level of significance is not a simple answer. As a researcher is preparing to conduct his experiment, the level of significance is determined. This measures the strength of the evidence that is a prerequisite in the sample prior to rejecting the null hypothesis and come to the conclusion that the effect is significant. In other words, how rare the results are. Significance levels should be used during the testing of hypothesis to help figure out which one supports the data. This is often referred to as the probability of obtaining the results by chance. A lower value score is the goal and this range is .05 to .10. For example, the .05 is equivalent to 5%. This would be denoted by an “a” or alpha symbol. This is also referred to as the critical region. Using the 5% as an example, during a study, one would expect to obtain a sample mean that falls in the critical region 5% of the time. This is also called the rejection region. It is essential to know if one should accept or reject the null hypothesis.

FORUM POST 2: Main Forum Post: P-Factor

Hello Class,

To understand the p-value in statistics, you first have to understand what a hypothesis and null hypothesis is. A hypothesis can be thought of as an idea, theory, or possible explanation that you wish you test, as to whether or not it holds true under the scrutiny of the scientific method. Conversely, a null hypothesis suggests that there is no statistical significance between the variables you are testing in your hypothesis. Simply put, it assumes the hypothesis you are testing is false.

With this in mind, the p-value gives us a way to gauge the statistical significance of the hypothesis and null hypothesis. It is also often referred to as the probability value, meaning the likelihood that a higher than chance outcome of the hypothesis is true. The generally excepted values of ‘p’ that prove significant are 5% or less. Thus, if the p-value is 5% or less, (p < 0.05) then there is strong indication that the null hypothesis is false and can be rejected. This in turn implies that there is a high probability that the hypothesis you are testing is true. Specifically, if p < 0.05, then there is less than a 1 in 20 chance that the hypothesis being tested is false. In fact, the smaller the p-value, the higher the probability that the hypothesis is true. For example, if the value of ‘p’ is less than 0.001 (p < 0.001), this would mean that the hypothesis being tested has less than a one in a thousand chance of being false. Thus, the smaller the p-value, the stronger the certainty that the null hypothesis is false and should be rejected.

FORUM POST 3: Dog tail 1 & Dog tail 2

Class,

What is the difference between a one-tailed and a two-tailed test and whether one is more rigorous than the other

Well, guess I should have read the forum before reading the chapter because I didn’t know it was going to be a “pick the best ones before they do!”
However, “what is the difference between a one-tailed and a two-tailed test and whether one is more rigorous than the other?”

Testing hypothesis one-tailed would be like, someone pushing a COVID-19 cure of “hydroxychloroquine” because it “could be” effective. Determining whether a claim is true/correct or not. Picture a bell curve on a graph the center of the bell is the Mean, a population Mean could be 100 thousand people. A one-tail test “could” show that the drug “has” or “does not have” an effect because the first or last part of the bell curve is very low. It’s one sided of the bell curve but not both ends.

A two-tail test would show both ends of the bell curve with the “most effective” or “least effective.” Two-tail shows the grater than or less than end effects of the mean data. The two-tailed test would show both ends of the bell curve. If in doubt of the numbers always run a two-tailed because it shows both ends of the data. A one-tail test could be used to show what someone wanted show.

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