Posted: July 9th, 2022

Discuss why this is important in your practice and with patient interactions

Provide two different examples of how research uses hypothesis testing, and describe the criteria for rejecting the null hypothesis. Discuss why this is important in your practice and with patient interactions.

Please provide two references
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Hypothesis testing is a common statistical method used in research to determine the likelihood that a specific hypothesis is true or false. Here are two examples of how research uses hypothesis testing:

Example 1: A study is conducted to examine the effectiveness of a new medication for reducing blood pressure in patients with hypertension. The research hypothesis is that the medication will significantly reduce blood pressure in patients with hypertension. To test this hypothesis, the study compares the blood pressure of a group of patients who take the medication to a group of patients who take a placebo. If the blood pressure of the group taking the medication is significantly lower than the blood pressure of the placebo group, the research hypothesis is accepted and the null hypothesis (that the medication has no effect on blood pressure) is rejected.

Example 2: A study is conducted to determine whether a specific type of therapy is effective in reducing anxiety in patients with generalized anxiety disorder. The research hypothesis is that the therapy will significantly reduce anxiety in patients with generalized anxiety disorder. To test this hypothesis, the study compares the anxiety levels of a group of patients who receive the therapy to a group of patients who receive a different type of therapy or no therapy at all. If the anxiety levels of the group receiving the therapy are significantly lower than the comparison group, the research hypothesis is accepted and the null hypothesis (that the therapy has no effect on anxiety) is rejected.

The criteria for rejecting the null hypothesis in hypothesis testing is typically based on a predetermined level of statistical significance, which is a measure of the likelihood that the observed results occurred by chance rather than being caused by the intervention being studied. In most cases, a p-value of 0.05 or less is considered statistically significant, meaning there is a less than 5% chance that the observed results occurred by chance. If the p-value is less than the predetermined level of statistical significance, the null hypothesis is rejected and the research hypothesis is accepted.

This is important in research and practice because it allows us to make informed decisions about the effectiveness of interventions and treatments based on evidence rather than assumptions or preconceived notions. In patient interactions, understanding the results of hypothesis testing can help healthcare professionals provide the best possible care and make informed recommendations to patients based on the most up-to-date research. It can also help to improve patient outcomes by identifying interventions that are most likely to be effective and reducing the use of ineffective or potentially harmful treatments.

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