Hypothesis testing and confidence intervals

Solution

Hypothesis testing and confidence intervals

The hypothesis is a question that a researcher would want to answer, the clinical inquiry in healthcare, the research design, how the data is gathered and analysed, is determined through the hypothesis. The main aim in healthcare is to find the correlations and answers within the data so as to provide better patient population outcomes. Correlations does not show the causes. Clinical significance is used to determine whether the research has a practical application to an individual or group. It also is used to determine health care decisions made by management. Clinical significance is applied in improving the quality of life of an individual and connects health research to patient care.

The confidence interval helps to reject the null hypothesis. The confidence interval is an interval estimate for the mean. It is a range of values that are set close to the mean either in a positive or negative direction. For the null to be rejected, 95% of the values need to be set close to the mean. The range of values determines the effect.

While there is not 100% certainty that either of these possibilities could be true, the Confidence Interval reflects the risk of the researcher being wrong. It is important that the statistical analysis of the data and its associated probability are true. The basis for rejecting or failing to reject the null hypothesis is based on the confidence interval of 95%. A confidence interval of 95% says that 95% of research projects like the one completed will include the true mean, but 5% will not, meaning that there are five chances in 100 of being wrong. Reducing the confidence interval increases the risk for error.

A confidence interval informs the investigator and the reader about the power of the study and whether or not if the data is compatible with a clinically significant treatment effect. Confidence intervals also provide a more appropriate means of analysis for studies that seek to describe or explain, rather than to make decisions about treatment efficacy. The logic of hypothesis testing uses a decision-making mode of thinking which is more suitable to randomized controlled trials of health care interventions. Hypothesis testing to determine statistical significance was initially intended to be used only in randomized experiments such as Randomized Control Trials which are typically not feasible in clinical research involving identification of risk factors, clinical diagnosis, or prognosis. The use of Confidence Intervals allows for hypothesis testing and it allows a more flexible approach to analysis that accounts for the objectives of each investigation.

The use of hypothesis testing and confidence intervals can be seen in quality improvement projects throughout an organization. In healthcare, we aim to find correlations and answers to our questions (hypothesis) within the data to provide better patient outcomes. Through these projects, we ask the question, find, plan and implement processes or the evidence, and evaluate the outcomes by building a concept or framework for the investigation.