Question

Write your thoughts on this discussion.

It is safe to say that if a person’s life depended on a good decision. The person making the decision would do the necessary collection of data to get it right! Making decisions in business is critical and the use of hypothesis testing can aid deciding. The word “hypothesis” has to do with making a claim about something that for the most part, on the face of it looks logical (Bateh, 2015). Hypothesis testing involves drawing inferences about two contrasting propositions relating to the value of a population parameter, such as a mean, proportion, standard deviation, or variance (Evans, 2013). A person could make a claim or assume that in a 10-mile radius that there are only German shepherd dogs in the neighborhood. The person who is observing this is basing what is seen during walking only in a certain area. But this is not telling the whole story about the area’s 10-mile radius. In doing hypothesis testing, one must prove or disprove the hypothesis and do several experiments to truly find out the truth or falseness of what the person assumes about German shepherds in the area (Bateh, 2015). This is a real-world scenario and I’m pretty sure that someone in this course has at least seen one German shepherd in the neighborhood. The fact of the matter here is that if there were sightings of the dog in the neighborhood, a person uses logic to determine the dog’s presence. The logic is known as common sense. First, a pattern is noticed, Second, it is noted that the pattern might has happen by chance. Third, a reason to believe that it is not by coincidence, and lastly, it is concluded that the pattern did not happen by chance (Miller, 2017). Parametric statistics require that there are fundamental assumptions met to be able to use a given statistic with a data set. The process of a parametric statistic happens when sample characteristics are used to estimate population parameters (Martin, 2012). Such characteristics are from the known normal distribution. Now, nonparametric statistics does not require the fundamental assumptions that are used in parametric statistics. The fundamental assumptions are the differences between the two methods (Martin, 2012).

Answer 1

Decision-making plays a major role in business activity and hypothesis acts as a helping hand for formulating strategies to make necessary decisions. Hypothesis is the first step of any research process whose basic function is the prediction of the final result of investigation. Hypothesis plays a major role in before formulating any strategies because it helps to assume the pre result for the situation.

What is Hypothesis?????

• A hypothesis is defined as making an assumption about the relation between variables.
• Hypothesis is derived from Greek word "Hypothenai" which means " to suppose" or "to put under".
• It is a tentative solution to a problem. Hypothesis is the prediction of the result in the mind of the researcher before the outcome at the time of research.
• In hypothesis the individual makes assumptions to prove or disprove something with the help of research. The validity of the hypothesis is usually unknown.

For example: From the given piece the person assumes that there are only German shepherd dogs in the 10- miles area. But this assumption may not be true. Hypothesis is all about performing experiments and to find out the truth and falseness of a situation. In this scenario the person assumes that yes there is a dog and he might use common sense to prove his point.

When an individual tries to prove his point then it is called Alternative Hypothesis.

When an individual tries to disprove the point then it is called Null Hypothesis.

Process of hypothesis

1. Set up of hypothesis: Under this individual setup series of assumptions. For example, in the above scenario the individual assumes that the German Shepherd dog exists in the given area and notices the pattern for proving his hypothesis.

2. Setup of significance level: Significance level is related to null hypothesis. The size of rejection or critical regions is known as the level of significance. It ensures that obtained results are free from decision maker biasness.

3. Test statistics: In this method, one can select a suitable statistical test to analyse and prove his assumptions. In this he tries to find out that the pattern might have happened by chance or not. Statistical methods include mean, proportion, standard deviation, or variance.

4. Doing computation: After selecting the appropriate statistical test the researcher tests the performance of different calculations from random samples. Here researchers also compute the standard error . Researchers try to provide a reason to believe that things do not happen by coincidence.

5. Making decisions: After doing a series of tests and computation the researcher encoded the result and came to a point where he has to make a decision whether to accept or reject the null hypothesis. For example, in a given scenario the individual concluded that the pattern did not happen by chance.

Types of hypothesis

Hypothesis can be tested using two ways:

• Parametric test.
• Non-Parametric test.

Parametric test.	Non-Parametric test.
In the parametric test population is completely known.	In non-parametric test information about population is not known.
Specific assumptions are made regarding the population in parametric tests.	No assumptions are made regarding the population while doing non-parametric tests.
Null hypothesis is assumed on parameters of the population distribution.	The null hypothesis is free from parameters in non-parametric tests.
Test statistics is based on distribution in parametric tests.	Test statistics are arbitrary in nature.
Parametric tests are applicable only for variables.	Non-parametric tests are applicable for both variables and attributes.
There is no parametric test for nominal scale data.	Non-parametric tests do exist for nominal and ordinal scale data.
Parametric test is powerful in nature as it includes population distribution.	Non-parametric test is not as powerful as a parametric test because it doesn't include the population distribution.
The parametric test includes T-test, Z-test, F-test.	Non-parametric tests include Sign test, Run test for randomness, Mann-Whitney U test,etc.

"If you liked the answer please give an Up-vote, this will be quite encouraging for me, thank you!"