Statistics is necessary for scientific research because it allows researchers to analyze the empirical data necessary 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. All studies therefore use descriptive statistics to present appropriate use of statistical tests and validity of data interpretation. Although descriptive statistics cannot draw general conclusions and allow only limited interpretations, they are useful in understanding the sample studied and establishing an appropriate framework for further analysis 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 potential areas of study in the future. Statistics are important to researchers because they allow them to study and interpret data more accurately, and researchers will notice trends in the data that would otherwise be overlooked and result in inaccurate and possibly subjective conclusions (Portney & Watkins, 2009). Frequency distribution is a method used in descriptive statistics to arrange 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 paper ...... losing relative to the population mean and the graph would display a normal curve because a sampling distribution always forms a normal curve (Portney & Watkins, 2009). When the frequency distribution graph shows a normal curve, it is possible to determine its variability and estimate the standard error of the mean according to the sample data. Unlike probability, an estimate of the population distribution allows researchers to establish the probability of selecting a sample with a predictable mean. Although the sampling distribution for predicting single outcomes is not applicable in reality, sample data can be used to draw inferences about the entire population from a sample, but they are not never used to directly measure variance. However, sample data find applications in several researches that require estimating unknown population parameters (Portney & Watkins, 2009)