Answer 2 for HLT 362 Evaluate and provide examples of how hypothesis testing and confidence intervals are used together in health care research

The confidence interval (CI) is an interval estimate for the mean and is a range of values that are set close to the mean in a negative or positive direction. For the null to be rejected, 95% of the values must be set close to the mean. The range of these values determines the effect. There is no certainty that either of these possibilities are true. The CI reflects the risk of the researcher possibly being wrong. The basis of the rejection or failure to reject the null hypothesis is based on the CI at 95%. A CI of 95% says that 95% of research projects like the one completed will involve the true mean, but 5% will not, which means that there are five chances in 100 of being wrong. If one reduces the confidence interval it will increase the risk of error. The CI is calculated by knowing the sample size, looking at the mean, the standard deviation and by choosing the level of confidence interval from the table. The calculation will give what your values will fall between in order to show 95% confidence (Helbig and Ambrose, 2022).

Healthcare providers using evidence-based medicine to inform practice must use clinical judgment to figure out the practical importance of studies from careful evaluation of the designs, sample size, likelihood of type I and type II errors, power, data analysis and reporting of statistical findings such as p values and 95% CI or both (Shreffler and Huecker, 2020).

In a hypothesis test there are two different hypotheses. Each one is attempted to be validated. Confidence intervals then will give a range of values that are more likely with a certain level of confidence. An example of this is testing new fall risk measures that have been implemented. The effect of the measures must be hypothesized, find the parameters on how well the measures are working on the patient population and then try to figure out the parameters on the success of the interventions.

References:

Shreffler, Jacob, and Martin R. Huecker. “Hypothesis Testing, P Values, Confidence Intervals, and Significance.” PubMed, StatPearls Publishing, 2020, www.ncbi.nlm.nih.gov/books/NBK557421/.

June Helbig & Jayme Ambrose. (2022). Applied Statistics for Health Care. Grand Canyon University (Ed.). (2022). Applied statistics for health care (2nd ed.).