Healthcare research encompasses the exploration of diverse data that significantly depend on hypothesis testing and confidence interval. Hypothesis testing is responsible for determining conclusions from a population sample and making decisions. For example, a value of H 0 is chosen to represent the null hypothesis while H A can represent the alternative hypothesis. When the value H 0 is rejected, the conclusion will be that H A is true. In case the value H 0 is found to be valid and not rejected, the conclusion will be that H 0 may be true. In contrast, Confidence Interval (CI) consists of the provision of the estimated range of values from the population parameter, which may be unknown (Javanmard & Montanari, 2014). Therefore, the estimated rage is calculated from the data set and believed to be true. For example, if eight poles have a height from x to y cm, then an interval of x plus or minus y confidence interval forms the value range and the confidence interval will be equal to x + y. The two elements are used together in the approximation of the values in a population and the calculation of the estimated set of values. Hypothesis testing is responsible for determining the value for prediction from the sample population under research. The value obtained through hypothesis testing is used to calculate the confidence interval (Greenland et al., 2016). When the sample population is given, and the need is to find the mean of values, hypothesis testing is necessary to determine the value for calculation. The value is then calculated using the confidence interval to determine the mean using the estimated set. For example, if medical researchers take a sample of 200-300 patients with a standard deviation of 0.2 and a 95 % confidence interval. This implies that hypothesis testing is used to determine the value of H A, by H0 + H1. The value obtained from hypothesis testing is incorporated in calculating the class interval using the formula (Prescott et al., 2014).
Where:
X is the mean
Z is the chosen Z-value from table values
s is the standard deviation
n is the number of observations
References
Greenland, S., Senn, S. J., Rothman, K. J., Carlin, J. B., Poole, C., Goodman, S. N., & Altman, D. G. (2016). Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations. European journal of epidemiology, 31(4), 337-350.
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Javanmard, A., & Montanari, A. (2014). Confidence intervals and hypothesis testing for high-dimensional regression. The Journal of Machine Learning Research, 15(1), 2869-2909.
Prescott, H. C., Langa, K. M., Liu, V., Escobar, G. J., & Iwashyna, T. J. (2014). Increased 1-year healthcare use in survivors of severe sepsis. American journal of respiratory and critical care medicine, 190(1), 62-69.
