# Week 4 Hypothosis Testing

One part is due tonight

Post a total of 3 substantive responses over 2 separate days for full participation. This includes your initial post and 2 replies to other students. You have two due dates for your responses. The first response is due by Thursday and the other responses are due by Monday. Note that you can always post more than 3 responses to the weekly discussion and your responses can be posted prior to the due dates.

Due by 10pm tonight Cst

Respond to the following in a minimum of 175 words:

Review the 8 step hypothesis test procedure described in Chapter 9. This week, we are reviewing how to conduct hypotheses test on one sample data. There are a number of concepts introduced here. Discuss your takeaways, questions, insights on the following:

1. Null and Alternate hypothesis – The importance of stating the null and alternative hypotheses before conducting the test.

2. Level of significance, also referred to as alpha.

3. One or Two tailed test – The difference between a one tail and a two tailed test.

4. Rejection Region.

5. Critical value.

6. Type 1 Error. – The importance of a type one error (p) in conducting the test

7. Type 2 Error.

Due by Monday

The common method to decide when to reject the null hypothesis is to compare the observed significance level (the p-value) with the research design significance level (the alpha). The p-value is therefore also referred to as the observed level of significance.

Explore the use of the NORM.S.DIST Excel function to find p-values. For example, for a 1 tail test, using the left tail, find the p-value corresponding to an observed z value of 2.05? Is your p-value 2%? If the alpha is 5% what can you infer regarding the null hypothesis?

Post your examples of null and alternate hypotheses and conditions under which the null hypothesis is rejected.

By Ronald

How to Conduct Hypotheses Test on One Sample Data.

Our reading discussed a hypothesis, which is a statement of what the researcher believes will be the outcome of an experiment or study. There are actually 3 types of hypotheses that were discussed; (1) Research Hypothesis, (2) Statistical Hypothesis, and (3) Substantive Hypothesis. The statistical hypothesis is a more formal one and has 2 types of hypothesis, the null hypothesis and the alternative hypothesis, and is constructed to contain all possible outcomes of the experiment or study. The Null hypothesis says conditions exists, there is nothing new happening, the old theory is still true, the old standard is correct, and the system is in control. The alternative hypothesis says that there is a new theory is true, there are new standards, the system is out of control, and/or something is happening.

In the research process, researchers begin with some hypothesis based on reasoning and evidence that they observe on starting data and then the entire research is based on their assumptions. Doing this in the beginning would determine the validity of a statistical claim.

References: Black, K. (2017). Business Statistics: For Contemporary Decision Making, (9th Edition). Hoboken, NJ. Wiley

Rumsey, D.J. (n.d.) How To Set Up a Hypothesis Test: Null versus Alternative. Retrieved from Dummies: https://www.dummies.com/education/math/statistics/…

By Richard

Professor & Class,

There are some pros and cons to both the one-tailed and the two-tailed test. For the one-tailed test, the pros are that they require less traffic and can gain significance faster. The cons of the one-tailed test are that it only accounts for one scenario and can lead to inaccurate and biased results. On the other hand for the two-tailed test, the pros are that it accounts for multiple scenarios, and can lead to accurate and reliable results. There are some cons which includes it requires more traffic and takes longer to gain significance.

The true difference between the two is that the one-tailed test allows you to determine if one mean Is greater or less than the another mean, but not both. A one-tailed test will illustrate to you the effect of a change in one direction and not the other. While the two-tailed allows you to evaluate the if two means are different from one another. In other terms, a two-tailed test will take into consideration the possibility of both a positive and negative effect.

In conclusion, one-tailed tests should be utilized only when you are not concerned with missing an effect in the untested direction. Two-tailed tests should be leveraged when you are willing to accept the outcomes of one mean being greater, lower, or similar to the other.

References

Vallee, K. (2015). The Difference Between One-Tailed & Two-Tailed Testing. Retrieved from https://blogs.oracle.com/marketingcloud/the-differ… 