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Drawing conclusions on the difference between the populations by their data samples is often necessary in statistics. The determination of the estimation of the difference of means an proportions between two normally distributed data populations is the objective for this participation. By doing so, we are learning to apply our knowledge about confidence intervals and significant tests to situations that involve the comparison of the two samples, we can determine if there (or what it is) a significant difference amongst the two populations. The statistical inference is the process through which inferences in regards to a population are formed based on certain statistics calculated from a sample of a drawn from that populations, which is cited from the Principles and Practice of Clinical Research in 2012. The statistical inference is important in order to analyze data properly. It is necessary to analyze data properly for interpretation of research result purposes and in order to draw appropriate conclusions. There are three basic statistical concepts, which are: effect estimate, confidence interval, an P-value. These concepts are applied in order to make comparisons of proportions, means, and medians. The Regression model is commonly used as the method for determining the description of the relationship between an outcome variable and one or more exposure variables. There are various types of regression. Some in which include: linear regression, logistic regression, median regression, Poisson regression, and Cox regression analyses. Statistical inference refers to the process of drawing conclusions from the model estimation and consists in the use of statistics to draw conclusions about some of the unknown aspect of a population based on random sample from that population. Statistical interference can be divided into two areas, which are: estimation and hypothesis testing. In estimation, the goal is to describe an unknown aspect of a population, for example, the average scholastic aptitude test (SAT) writing score of all examinees in the State of California in the USA. Estimation can be of two types, point estimation and interval estimation, depending on the goal of the application. The goal of hypothesis testing is to decide which of two complementary statements about a population is true.

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