PUB-550 Topic 3: Hypothesis Testing DQs

PUB-550 Topic 3: Hypothesis Testing DQs

Topic 3: Hypothesis Testing

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Data Resource Document

1. CDC WONDER

Explore the CDC WONDER website.

https://wonder.cdc.gov/

2. Gapminder

Explore the Gapminder website.

http://www.gapminder.org/

3. Behavioral Risk Factor Surveillance System

Explore the Behavioral Risk Factor Surveillance System, located on the CDC website.

https://www.cdc.gov/brfss/index.html

4. PovcalNet

Explore the PovcalNet: An Online Analysis Tool for Global Poverty Monitoring page of The World Bank website.

http://iresearch.worldbank.org/PovcalNet/home.aspx

5. Demographic and Health Survey Data

Explore the Demographic and Health Survey (DHS) data website.

http://dhsprogram.com/data/

6. Alcohol-Related Disease Impact Application

Explore the Alcohol-Related Disease Impact Application (ARDI) page of the CDC website.

https://nccd.cdc.gov/DPH_ARDI/default/default.aspx

7. Public Health Partners

Explore the Health Data Tools and Statistics page of the Public Health Partners website. This site provides many public health data sources.

https://phpartners.org/health_stats.html

8. National Health Interview Survey

Explore the National Health Interview Survey page of the CDC website. Review the documents.

https://www.cdc.gov/nchs/nhis/index.htm

9. Global School-Based Student Health Survey

Explore the purpose and methodology of the international Global School-Based Student Health Survey, located on the World Health Organization (WHO) website.

http://www.who.int/chp/gshs/en/

10. Youth Risk Behavior Surveillance System

Explore the methods, data, and documentation of the Youth Risk Surveillance System, located on the CDC website.

https://www.cdc.gov/healthyyouth/data/yrbs/

Topic 3 DQ 1

Hypothesis testing is used to evaluate a hypothesis about an entire population (Corty, 2016). Hypothesis testing is extremely statistic based with makes it objective not subjective. Statisticians use hypothesis testing to make sure that decisions remain data-based and not emotional based. The data from hypothesis testing is from the sample used to evaluate a population. There are six steps in hypothesis testing (Corty, 2016):

1. Pick the right statistical test (TEST)
2. Check the assumptions to ensure it is okay to do the test (ASSUMPTION)
3. List all null and alternative hypothesis options (HYPOTHESIS)
4. Find the value of the static that determines when to reject or accept the null hypothesis (DECISION)
5. Calculate the value of the test statistics (CALCULATION)
6. State what the results are (INTERPRETATION)
7. PUB-550 Topic 3: Hypothesis Testing DQs

In every hypothesis test, the outcomes are dependent on a correct interpretation of the data (Illowsky, n.d.). Incorrect calculations or misunderstood summary statistics can lead to errors that affect the results. A Type I error occurs when a true null hypothesis is rejected (Illowsky, n.d.). A Type II error occurs when a false null hypothesis is not rejected (Illowsky, n.d.).

The probabilities of these errors are denoted by the Greek letters α and β, for a Type I and a Type II error respectively (Illowsky, n.d.). The power of the test, 1 – β, quantifies the likelihood that a test will yield the correct result of a true alternative hypothesis being accepted (Illowsky, n.d.). A high power is desirable.

Formula Review (Illowsky, n.d.).

α = probability of a Type I error = P(Type I error) = probability of rejecting the null hypothesis when the null hypothesis is true. PUB-550 Topic 3: Hypothesis Testing DQs

β = probability of a Type II error = P(Type II error) = probability of not rejecting the null hypothesis when the null hypothesis is false.

1. The decision is not to reject H0 when H0 is true (correct decision).
2. The decision is to reject H0 when H0 is true (incorrect decision known as a Type I error).
3. The decision is not to reject H0 when, in fact, H0 is false (incorrect decision known as a Type II error).
4. The decision is to reject H0 when H0 is false (correct decision whose probability is called the Power of the Test).

Example (Illowsky, n.d.):

It’s a Boy Genetic Labs claim to be able to increase the likelihood that a pregnancy will result in a boy being born. Statisticians want to test the claim. Suppose that the null hypothesis, H0, is: It’s a Boy Genetic Labs has no effect on gender outcome.

• Type I error: This result when a true null hypothesis is rejected. In the context of this scenario, we would state that we believe that It’s a Boy Genetic Labs influences the gender outcome, when in fact it has no effect. The probability of this error occurring is denoted by the Greek letter alpha, α.
• Type II error:This result when we fail to reject a false null hypothesis. In context, we would state that It’s a Boy Genetic Labs does not influence the gender outcome of a pregnancy when, in fact, it does. The probability of this error occurring is denoted by the Greek letter beta, β. PUB-550 Topic 3: Hypothesis Testing DQs

References

Corty, E. (2016). Using and interpreting statistics. A practical text for the behavioral, social, and health sciences 3^rd Edition. Retrieved from https://viewer.gcu.edu/GGdEcj

Illowsky, B. (n.d.). Introduction to Statistics. Retrieved from https://courses.lumenlearning.com/introstats1/chapter/outcomes-and-the-type-i-and-type-ii-errors/

opic 3 DQ 1

Banerjee et al., (2009) study found the following: Hypothesis testing is an important activity of empirical research and evidence-based medicine. A well worked up hypothesis is half the answer to the research question. For this, both knowledge of the subject derived from extensive review of the literature and working knowledge of basic statistical concepts are desirable. The present paper discusses the methods of working up a good hypothesis and statistical concepts of hypothesis testing (p.127)

Banerjee et al., (2009) study found the following: Just like a judge’s conclusion, an investigator’s conclusion may be wrong. Sometimes, by chance alone, a sample is not representative of the population. Thus, the results in the sample do not reflect reality in the population, and the random error leads to an erroneous inference. A type I error (false-positive) occurs if an investigator rejects a null hypothesis that is actually true in the population; a type II error (false-negative) occurs if the investigator fails to reject a null hypothesis that is actually false in the population. Although type I and type II errors can never be avoided entirely, the investigator can reduce their likelihood by increasing the sample size (the larger the sample, the lesser is the likelihood that it will differ substantially from the population) (p.127). PUB-550 Topic 3: Hypothesis Testing DQs

Banerjee et al., (2009) study found the following: False-positive and false-negative results can also occur because of bias (observer, instrument, recall, etc.). (Errors due to bias, however, are not referred to as type I and type II errors.) Such errors are troublesome, since they may be difficult to detect and cannot usually be quantified (p.127).

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. doi:10.4103/0972-6748.62274