Confidence Intervals Rather Than P ValuesThe hypothesis is a conjecture that is accepted in order to interpret a certain phenomenon or give guidance for further investigation. In this case, it may be proven wrong or correct through hypothesis testing procedures. The testing involves using confidence intervals (CI) or P Values. Confidence intervals are a range of values that are calculated statistically and contain a true population parameter, for example, mean or difference between means (du Prel et al., 2009). In contrast, P values refer to the probability of finding observation or so extreme, given the null hypothesis is true. Unlike P Values, confidence intervals give the direction of effect at a given confidence level. Values excluded from the confidence interval are not likely to exist in the population and are termed as improbable. As such, Confidence intervals eliminate the uncertainties in graphical presentations caused by standard errors since they are confounded by the number of observations (Greenland et al., 2016). P values have no units, whereas confidence intervals are in the units of the dependent variable, which makes result interpretation much easier.Confidence intervals are preferable to use since they illustrate the range of plausible differences of associations and effects between study groups. That way, they aid in determining whether or not the observed differences reveal true benefits as well as the superiority of one treatment over the other (Pandis, 2013). This provides valuable information for clinical decision making. Whereas P Values are known to confine the interpretation of trial outcomes, confidence intervals move ahead to interpret the results to the size of the association and effect, and the range of plausible values provided with the data under study.The confidence intervals are very necessary for the analysis of the given statistics. Since the market is mostly based on the risk, confidence intervals are related to those risks. They provide us with a range by analyzing the sample population size and the potentially possible variation. (Attia, 2005)This range is an estimate of where the real answer may be. Moreover, the p-value is necessary because the value of the p-value decides whether the null hypothesis has to be rejected or it is acceptable. Whatever the result is about the acceptability of the null hypothesis then decides about the difference in the results of the control group and the sample population. If the hypothesis is accepted, it means that the outcomes of the control group and the group studied were not different. However, if this rejected it means otherwise. Also, the p-value indicates how spontaneous the results are. It indicates that the results produced were by chance, not deliberate. To know the possible effect of any intervention to a sample population is done by chance or is a clear cut outcome, we have calculated the p-value. The probability will help us decide how far the results are real. Compared to the p-value, the confidence interval is more reliable. (Prel, Hommel, Röhrig, & Blettner, 2009) This is why, because it provides more certain and true answers. Also, it helps take results from unfamiliar population parameters. Its significance lies in the fact that it can be utilized in the testing of the hypothesis as well as a p-value to statistically analyze and assess any estimate. Such is the example of the use of the confidence interval in making the clinical decisions. For the healthcare transformation conclusions based on evidence is necessary(He & Fineout-Overholt, 2016). To evaluate research effectively, one should be able to determine its validity and reliability. This requires the analysis of the CI as it indicates the level of uncertainty in any research.Referencesdu Prel, J. B., Hommel, G., Röhrig, B., & Blettner, M. (2009). Confidence interval or p-value?: part 4 of a series on evaluation of scientific publications. Deutsches Ärzteblatt International, 106(19), 335.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: aPandis, N. (2013). Confidence intervals rather than P values. American journal of orthodontics and dentofacial orthopedics, 143(2), 293-294. guide to misinterpretations. European journal of epidemiology, 31(4), 337-350.Attia, A. (2005). Why should researchers report the confidence interval in modern research? Middle East Fertil Soc J, 78-81.He, Z., & Fineout-Overholt, E. (2016, March). Understanding confidence intervals helps you make better clinical decisions. Retrieved from American Nurse Today: https://www.americannursetoday.com/understanding-confidence-intervals-helps-make-better-clinical-decisions/Prel, J.-B. d., Hommel, G., Röhrig, B., & Blettner, M. (2009). Confidence interval or p-value?: part 4 of a series on evaluation of scientific publications. Deutsches Ärzteblatt International, 335.I NEED TO ANSWER THE PROFESSOR QUESTION;Tammy: Thanks for the read. The Centers for Disease Control and Prevention (CDC) explains that public health surveillance is the systematic collection, analysis, interpretation, and dissemination of health data on an ongoing basis, to gain knowledge of the pattern of disease occurrence and potential in a community, in order to control and prevent disease in the community. Based on your survey of the literature in the support of your ideas, which of the statistical package reviewed seems more appropriate for the purpose of surveillance in an area of your public health interest?