Biostatistical inference is about the sample; the population parameters, like the mean, remain estimates, implying the uncertainty of absolute population parameters. All biomedical and clinical inferences about samples studied carry some uncertainties, requiring caution in the interpretation of findings for application to improvement of care or suggestions of diagnostic and screening guidelines. A parameter in biostatistics is a number that describes the population, while the population is the entire group of individuals or animals about which we require information or data. Ideally, the parameter is a fixed number, but in reality, this value remains unknown. Since the parameter from the population is unknown, statistics is used to describe the sample. The sample is a part of the population from which researchers obtain data, which they used to draw inferences or conclusions about the population. While the value of the statistic is known, this value is not fixed and can change from sample to sample. Based on this sample-to-sample variability, all findings have underlying uncertainties, and population parameters remain unknown, implicative of extreme caution in the interpretation and application of study findings in the improvement and provision of quality medical and psychosocial care to our patients.1