Decision makers need to know whether results are due to chance or some factor of interest.

Full Answer Section

  The interpretation of p-values can be complex, and there are a number of factors that can affect the meaning of a p-value. For example, the size of the sample can affect the p-value, and a larger sample size will generally result in a lower p-value. Additionally, the effect size of the difference between the groups can also affect the interpretation of the p-value. Examples of Statistical Significance and P-Value in Health Care There are a number of examples of statistical significance and p-value in health care. For example, a study might compare two different treatments for a particular disease. The study might find that the treatment with drug A is more effective than the treatment with drug B. The p-value for this study would be a measure of the statistical significance of the difference between the two treatments. Another example of statistical significance and p-value in health care is the use of diagnostic tests. A diagnostic test is used to determine whether a person has a particular disease. The p-value for a diagnostic test is a measure of the probability of getting a positive test result if the person does not have the disease. Practical and Administrative Implications of Statistical Significance and P-Value The concepts of statistical significance and p-value have a number of practical and administrative implications in health care. For example, p-values are often used to determine whether a new treatment is effective. If the p-value for a new treatment is less than or equal to 0.05, then the treatment is considered to be statistically significant. This means that there is less than a 5% chance that the results of the study occurred by chance. P-values are also used to determine the accuracy of diagnostic tests. If the p-value for a diagnostic test is low, then the test is considered to be accurate. This means that there is a low probability of getting a false positive or false negative result. In addition to their use in research, p-values can also be used to make decisions about patient care. For example, a doctor might use the p-value for a diagnostic test to determine whether to order further testing or to start treatment. Conclusion The concepts of statistical significance and p-value are important in health care. They can be used to determine whether the results of a study are due to chance or to a real difference between the groups being compared. P-values can also be used to determine the accuracy of diagnostic tests and to make decisions about patient care. It is important to note that p-values are not the only factor that should be considered when making decisions about research or patient care. Other factors, such as the size of the effect and the clinical significance of the results, should also be considered. However, p-values can be a useful tool for making these decisions.

Sample Answer

    In statistics, statistical significance is the probability of obtaining a result by chance. The p-value is a measure of statistical significance, and it is calculated by comparing the observed results to the results that would be expected if there was no real difference between the groups being compared. A p-value of less than or equal to 0.05 is generally considered to be statistically significant, which means that there is less than a 5% chance that the observed results occurred by chance. However, it is important to note that a p-value of 0.05 does not mean that the results are certain to be due to a real difference between the groups. It simply means that the results are unlikely to have occurred by chance.