results reporting

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In many different situations, you may be exposed to information that requires further analysis; this may be true as you receive reports, read a journal, or prepare for a presentation. What is certain is that you should always approach results in a critical but unbiased way, and be prepared to address any potential weaknesses in the reported data.

For this Discussion, you will evaluate recommendations made based on the results of a study from a given scenario.

To prepare:

Select one of the following scenarios:

  • Scenario 1: A sample of 5,000 swimmers and 5,000 runners were screened for cardiovascular disease (CVD). The study found that the rate of CVD is 33.3% in swimmers and 30.9% in runners, with the p-value of <0.05. A public campaign is recommended to promote running instead of swimming activities.
  • Scenario 2: A study of 1,000 married women was conducted concerning pre-marital birth and the success of the subsequent marriages. The study reported that less educated women were 30% more likely to have children before marriage than highly educated women. In addition, 40% of the African-American women in the study had children before marriage. The study concluded that marriage counseling should be provided to less educated women and to African-American women.

Post an evaluation of the recommendation from your scenario. Include the following in your post:

  • The scenario number you selected
  • Your evaluation of whether the recommendations are overstated or understated based on the results described in the scenario (justify your response)
  • Your suggestion of two methods for addressing and correcting those errors

Support your post with the Learning Resources and current literature. Use APA formatting for your Discussion and to cite your resources.

References

https://class.waldenu.edu/bbcswebdav/institution/USW1/201950_27/PH_PUBH/PUBH_8545_WC/readings/USW1_PUBH_8545_Week10_wantedValidAndMeaningfulData.pdf

Fullerton, S. M., & Lee, S. S. (2011). Secondary uses and the governance of de-identified data: Lessons from the human genome diversity panel. BMC Medical Ethics, 12(1), 16.

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