*PUB 550 Summarize the six steps of hypothesis testing*

PUB 550 Summarize the six steps of hypothesis testing

“Hypothesis testing is the process used to evaluate the strength of evidence from the sample and provides a framework for making determinations related to the population,”(Davis, 2006)

Step 1: State your null and alternate hypothesis

Step 2: Collect data

Step 3: Test statistic

Step 4: Deciding if the hypothesis will be rejected or fail to reject

Step 5: Conclusions

Hypothesis tests are often used in clinical trials to determine whether a change of habits, new treatment, drug, or procedure can have a positive impact on the outcome in patients. For example, it is recommended to have daily physical activity. Physical activity is essential to the maintenance of one’s health, as it helps prevent a variety of issues. “Obesity is the most prominent concern, but according to the Centers for Disease Control and Prevention (CDC) (n.d.), regular exercise can help reduce chronic and life-threatening conditions.”(Lazarus, 2021) There are a lot more benefits besides improved health, including lower medical bills and a better way of living.

### References:

Davis, R. B., & Mukamal, K. J. (2006). Hypothesis Testing. *Circulation*, *114*(10), 1078–1082. https://doi.org/10.1161/circulationaha.105.586461

Lazarus NR, Harridge SDR. A Hypothesis: The Interplay of Exercise and Physiological Heterogeneity as Drivers of Human Ageing. Front Physiol. 2021 Sep 9;12:695392. doi: 10.3389/fphys.2021.695392. PMID: 34566675; PMCID: PMC8458865.

Ranganathan (2019) mentions that researchers cannot carry out studies on whole populations so samples are used. This is very useful for hypothesis testing to help with decision making. The testing begins with having the assumption which is used as a null hypothesis and another opposite alternative hypothesis. We need to look out for errors when rejecting or accepting the null hypothesis. Hypothesis testing is used to take a look at the strength of evidence from the sample and provides areliable method for understanding of the whole population based on the sample and how reliable and valid the conclusions. We want to make sure the hypothesis whether we reject or accept the null/alternative hypothesis that supports the data that was found.

__References__:

Ranganathan P, Cs P. An Introduction to Statistics: Understanding Hypothesis Testing and Statistical Errors. Indian J Crit Care Med. 2019 Sep;23(Suppl 3):S230-S231. doi: 10.5005/jp-journals-10071-23259. PMID: 31656385; PMCID: PMC6785820.

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### REPLY

#### There are 5 main steps in hypothesis testing:

- State your research hypothesis as a null hypothesis and alternate hypothesis (Ho) and (Ha or H1).
- Collect data in a way designed to test the hypothesis.
- Perform an appropriate statistical test.
- Decide whether to reject or fail to reject your null hypothesis.
- Present the findings in your results and discussion section.

#### Senerio:

Vitamin C

Is it true that vitamin C has the ability to cure or prevent the common cold? Or is it just a myth? There’s nothing like

an in-depth experiment to get to the bottom of it all. A potential hypothesis test could look something like this:

- Null hypothesis – Children who take vitamin C are no less likely to become ill during flu season.
- Alternative hypothesis – Children who take vitamin C are less likely to become ill during flu season.
- Significance level – The significance level is 0.05.
- P-value – The p-value is calculated to be 0.20.
- Conclusion – After providing one group with vitamin C during flu season and the other with a placebo, you record whether or not participants got sick by the end of flu season. After conducting your statistical analysis on the results, you determine a p-value of 0.20. That is above the desired significance level of 0.05, and thus you fail to reject the null hypothesis. Based on your experiment, there is no support for the (alternative) hypothesis that vitamin C can prevent colds.

### Reference:

The first step when it comes to the six steps of hypothesis testing first goes with setting up the hypothesis. There are two hypotheses: the null hypothesis and the alternative hypothesis. The first hypothesis is considered valid until proved wrong with substantial evidence. The second hypothesis is known as the alternative hypothesis, which can also be referred to as the a research hypothesis also. When you test this step out, you must be sure to be aware of any possible outcomes that may happen, interfere or occur. The second step is to consider the level of significance, which deals with probability. The probability that is willing to be done to test for a wrongful assumption towards the null hypothesis is what is called the alpha value. The third step must be to correctly calculate the test statistic with the sample data that has been gathered. The fourth step is to then calculate the p-value, which refers to the probability value. To find this you must use the test statistic to find the probability of the data that is mainly producing the statistic or similar to that. This step is the most important in which you must in turn decide whether the null hypothesis is valid or incorrect due to your finding with testing out the sample data and calculations of the probability. The last step you must then state your findings in what led you to your hypothesis from testing and calculations for the test. A good scenario of testing a hypothesis for public health data would be for testing the effectiveness of a different medications.

6a.2 – Steps for Hypothesis Tests. Penn State. Eberly College of Science. https://online.stat.psu.edu/stat500/lesson/6a/6a.2 (2022, July 2)

The post is great keep in mind that When you are evaluating a hypothesis, you need to account for both the variability in your sample and how large your sample is. Based on this information, you’d like to make an assessment of whether any differences you see are meaningful, or if they are likely just due to chance**.** This is formally done through a process called hypothesis testing.

*Five Steps in Hypothesis Testing:*

- Specify the Null Hypothesis
- Specify the Alternative Hypothesis
- Set the Significance Level (a)
- Calculate the Test Statistic and Corresponding P-Value
- Drawing a Conclusion

If you do a large number of tests to evaluate a hypothesis (called multiple testing), then you need to control for this in your designation of the significance level or calculation of the p-value. For example, if three outcomes measure the effectiveness of a drug or other intervention, you will have to adjust for these three analyses.

Hypothesis testing is not set up so that you can absolutely prove a null hypothesis. Therefore, when you do not find evidence against the null hypothesis, you fail to reject the null hypothesis. When you do find strong enough evidence against the null hypothesis, you reject the null hypothesis. Your conclusions also translate into a statement about your alternative** **hypothesis.** ** When presenting the results of a hypothesis test, include the descriptive statistics in your conclusions as well. Report exact p-values rather than a certain range. For example, “The intubation rate differed significantly by patient age with younger patients have a lower rate of successful intubation (p=0.02).” Here are two more examples with the conclusion stated in several different ways.

### Reference cited

https://www.gvsu.edu/cms4/asset/4CE39E3F-BF40-0D23-0D675539A8F525E7/steps_to_hypothesis_testing.pdf

As identified by Frost(n.d.). Hypothesis testing uses data from a sample of a population to make assumptions about the properties of a population, Frost(n.d.). stated that samples from a population are typically an extremely small percentage of the entire population which occasionally causing a misrepresentation of the population.

There are six steps for Hypothesis testing

State your null and alternate hypothesis

It is important to state the hypothesis as a null or alternate so it can be tested

mathematically per Banjerjee et al the alternate hypothesis is usually your initial hypothesis that predicts a relationship between variables. Identified by Banjerjer et al the null hypothesis is a prediction of no relationship between the variables you are interested in.

Collect data

For a statistical test to be valid, it is important to perform sampling and collect data in a way that is designed to test you hypothesis as identified by Frost(n.d.). Frost stated that if you are not representative, then you cannot make statistical reference.

### Perform a statistical test

Statical test is based on a comparison of within group variance versus between group variance it is based off of how different the groups from one another.

### Decide whether to reject or fail to reject your null hypothesis

You would have to base this on the outcome of your data then the premise will rise as to reject or failing to reject your null hypothesis. It is important that you use the p-value made by your statistical test to determine your decision.

### Present your findings

You should give a brief summary of the data and the results of your statistical test, knowing whether to reject or failing to reject will be asked within your data or testing.

### Word the Statistical decision into readable format

During the statistical decision it should be readable and should logical thought processes based off of our hypothesis, knowing that we based the data off of logic the decision to move forward will be imperative.

A scenario in which a hypothesis can be tested is with Long Covid hypothesis and Covid-19, many people are still affected by the phenomena of long covid, According to the CDC, the colloquial term “Long Covid” is also known as Post-Acute Sequelae of SARS-CoV-2 or Post-Covid-Syndrome, CDC identifies PCS as sign and symptoms that develop during or after an infection consistent with Covid-19, continue for more than 12 weeks.

There are two or more aliments such as muscle fatigue, brain fog, inflamed toes, breathlessness, heart arrhythmias and there is more to be discussed as the years progress with this disease.

### Reference

Banerjee, A., Chitnis, U. B., Jadhav, S. L., Bhawalkar, J S., & Chaudhury, S. (2009). Hypothesis testing, type I and type II errors

Industrial psychiatry journal, 18(2), 127-131.

https://doi.org/10.4103/0972-6748.62274

CDC (Center for Disease Control) 2020 Long Covid

Frost, J. (n.d.). Types of Errors in Hypothesis Testing