Statistics are necessary for scientific research because they allow researchers to analyze the empirical data needed to interpret results and draw conclusions based on the research findings. According to Portney and Watkins (2009), all studies require a description of the subjects and the responses obtained by measuring central tendency, therefore all studies use descriptive statistics to present appropriate use of statistical tests and validity of data interpretation. Although descriptive statistics do not allow general conclusions and allow only limited interpretations, they are useful for understanding the study sample and establishing an appropriate framework for further analyzes of the study. Further analysis using appropriate statistical methods allows researchers to establish correlations between independent and dependent variables, define possible outcomes, and accurately identify areas for potential study in the future. Statistics is important to researchers because it allows them to investigate and interpret data more accurately, and researchers will notice patterns in the data that would otherwise be overlooked and lead to inaccurate and possibly subjective conclusions (Portney & Watkins, 2009). Frequency distribution is a method used in descriptive statistics to organize the values ​​of one or more variables in a sample, so as to summarize the distribution of values ​​in a sample. Frequency distribution is the most basic and frequently used method in statistics because it creates organized tables of data that can later be used to calculate averages or measure variability. The organized frequency distribution of data provides continuous data that is easier to work with than the raw data obtained... middle of the paper ......losing compared to the population mean and the graph would show a normal curve because a sampling distribution always forms a normal curve (Portney & Watkins, 2009). When the frequency distribution graph shows a normal curve, you can determine its variability and estimate the standard error of the mean in accordance with the sample data. Unlike probability, an estimate of the population distribution allows researchers to determine the probability of selecting a sample with a predictable mean. Although the sampling distribution for predicting individual outcomes is not applicable in reality, sample data can be used to draw inferences about the entire population from a sample, but it is never used to directly measure variance. However, the sample data find applications in various research that requires the estimation of unknown population parameters (Portney & Watkins, 2009)