Please post 3 reply responses of 90 or more words to your classmates
Responses should be a minimum of 90 words and include direct questions
1st Chander Sekar
Mason,
Null and alternative hypotheses are complements to each other – do you agree? What is meant by level of significance? What is its role in testing of hypothesis? How is it used along with the p-value for an accept/reject decision on the hypothesis tested? Thanks
2nd Daniel Malarski
A statistician will make a decision based off of confidence interval claims through a process called hypothesis testing. Before collection of data by the statistician, they will formulate two contradictory statements one being the alternative hypothesis and the other being the null hypothesis. In a general, a hypothesis test collects data from a population or sample from the population. Then based off of the data collected and the analysis conducted, the statistician will either accept or deny the null hypothesis. The reason the null hypothesis is accepted or rejected and not the alternative hypothesis is because if the null hypothesis is proven false, the alternative hypothesis must be true. If attempting to prove the null hypothesis there may be numerous criteria that the alternative hypothesis is proven for where there is only one rejecting criteria. Thus, proving of existing theories is far easier. I recommend and concur with the standard way of thinking in regards to the null and alternative hypothesis. In summary, to properly conduct a hypothesis test, the following steps are conducted in order:
State the Null Hypothesis and Alternative Hypothesis.
Null Hypothesis is originally assumed to be true and a complete contrary statement to that which is being tested and is proven either true or false
Alternative Hypothesis is the exact opposite to that of the nul hypothesis and can only be true if the null hypothesis is proven false.
State the Level of Significance.
This determines an accuracy rate for the hypothesis tester by choosing a range typically around 5%. This gives a level of confidence when either accepting or rejecting the null hypothesis.
Establish Critical or Rejection Region
The region with a definitive line that determines if the null hypothesis should be accepted or rejected based on the plot of the bell curve of the data.
Select the Suitable Test of Significance or Test Statistic
Chosen to based on probability distribution and confirms if there is a statistical noticeable difference between the null and alternative hypothesis.
Formulate a Decision Rule to Accept Null Hypothesis. (Sharma, 2022)
References
Sharma, J. K. (2022). Chapter 10- Hypothesis Testing. Business Statistics. Retrieved June 8, 2022, from https://learning-oreilly-com.ezproxy2.apus.edu/library/view/business-statistics-second/9789332503434/xhtml/chapter010-01.xhtml#head10.4
3rd Nashekia Parkins
Hypothesis testing involves various steps. These steps might vary from one research to another. However, the common steps are listed below (Prins & Kingdom, 2018):
Step 1: State hypothesis
Step 2: Plan an experiment that is appropriate for testing the hypothesis
Step 3: Assess and decide on the validity of the hypothesis
Step 4: Preview the hypothesis
Step 5: Present the results
There are various reasons why the null hypothesis is tested instead of the alternative hypothesis. The null hypothesis is the hypothetical case under which researchers have no explanation for the phenomenon researchers observe but that our explanations do not need to be true (Szucs & Ioannidis, 2017). Testing under the null hypothesis is different from testing under the alternative hypothesis. Testing under the null hypothesis means that researchers are ready to accept any explanation.
There has been a research debate about the use of alternative hypothesis testing. I recommend that the use of either alternative or null hypothesis be determined by the nature of the study one is undertaking. Researchers try to determine if a particular assumption is valid when testing a hypothesis. For example, if researchers hypothesized that eating a particular food would produce a specific response, researchers would expect the response to be the same when researchers tested the food without the hypothesis (Ortega & Navarrete, 2017). However, sometimes the response does not turn out to be the same. Instead of rejecting the hypothesis, researchers should try to understand why the hypothesis failed. If researchers understand why the experiment did not work, researchers know why the alternative hypothesis might be true. If not, then researchers cannot say anything about the hypothesis. The alternative hypothesis is the one that states that the data is not typical of the population. Researchers must test the null and alternative hypotheses and decide on the most plausible explanation.
References
Ortega, A., & Navarrete, G. (2017). Bayesian hypothesis testing: an alternative to null hypothesis significance testing (NHST) in psychology and social sciences. In Bayesian inference. IntechOpen.
Prins, N., & Kingdom, F. A. (2018). Applying the model-comparison approach to test specific research hypotheses in psychophysical research using the Palamedes toolbox. Frontiers in psychology, 9, 1250.
Szucs, D., & Ioannidis, J. P. (2017). When null hypothesis significance testing is unsuitable for research: a reassessment. Frontiers in human neuroscience, 11, 390.
