The concept of statistics has been growing over the years, and more advancements are expected in the future. In the past days of civilization, the government applied statistics in researching and analyzing administrative and economic records. Today, statistics is applied in all areas, including politics, planning, social science, medicine, and business. Business is among the primary areas that require intensive use of statistical data. Operating a business is a complex activity that requires long term planning development of new products and services and reaching to new customers while retaining the existing ones. Managers need knowledge of statistics for better management, especially when dealing with uncertainty and making significant decisions. Statistics research helps managers across a range of areas, among them financial analysis, marketing research, and auditing. Managers, customers, investors, and other stakeholders rely on statistics to recognize the performance of the business and predict future direction. Statistics as the collection, organization, presentation, analyzing, and interpretation of data. The paper will discuss five essential elements of statistics, descriptive statistics, inferential, hypothesis development and testing, selection of appropriate statistical tests, and evaluating statistical results.
Descriptive statistics comprises of various statistical tools that help in summarizing and organizing data for easy understanding. Descriptive statistics tools include charts and graphs used to display data making it easy to measure central tendency used in measuring midpoints of distribution ( McCarthy, McCarthy, Ceccucci & Halawi, 2019) . Descriptive statistics are used only used in describing data without making any inference on the data. Descriptive statistics are used by sociologists and researchers to describe a specified population adding up various features of particular data concerning the same sample. Thus, this statistic is essential when giving a summary of the population. The primary tools utilized by descriptive statistics to measure central tendency include mean, mode, and medium. The major disadvantage of descriptive statistics is the possibility of giving incorrect generalization of the given population. According to ( McCarthy (2019), statistical data should represent a sample with the proximity of the actual value of the population. Since descriptive fail to provide real value, it fails to be placed under the category of statistics. This drawback of descriptive statistics does not consider qualitative factors that impact the population, and this might lead to an incorrect conclusion.
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Inferential statistics is mainly used by sociologists when explaining and predicting the characteristics and behaviors of a particular group of people. The sociologist needs to observe and develop a hypothesis of the data collected to conclude the factual information. As a subset of mathematics, inferential statistics are used to analyses and interpret data. Estimating parameters and hypothesis tests are the primary areas of inferential statistics ( Byrne, 2007) . In estimating parameter, the sociologist or researcher take statistics from the sample data and use it to comment on a population parameter like mean. In the hypothesis test, the sociologist uses data from the sample to answer the research question. For instance, when researching the effectiveness of new cancer drugs, the researcher might use data available in samples to answer the research questions. The researcher takes a sample of data from the population and determines if the data predict whether new cancer drug works for everyone. Researchers use this statistic to test the hypothesis to determine the outcome of a study. The result of the statistics is then used to support their conclusion. The conclusion drawn explains whether the behavior is caused by chance or underlying phenomenon ( Lowry, 2014) . Various statistical tools used in inference are appropriate for specific study design. The three primary statistical tools used in inferential statistics include t-test, regression analysis of variance (ANOVA), and z statistics.
Development and testing of hypothesis is the crucial element of statistics. A hypothesis is a declarative statement used to depict the relationship between dependent and independent variables. The purpose of the hypothesis is to provide a tentative explanation of the phenomenon and spread knowledge of a particular area and to give direction to the research ( Mourougan & Sethuraman, 2017) . The first step in the development of the hypothesis is to develop research questions. Every research question needs to have an independent and dependent variable. The independent variable shows the degree of similarity between subjects, while dependent variables show who the subject befriends. There are two types of hypothesis used by analysts. First is the null hypothesis (Ho), which is a statement that denies the difference between the experiment condition and the status quo. For example, “there is no relationship between smoking and increased cases of chronic diseases. If the hypothesis testing shows that there is a relationship between smoking and cases of cancer, it becomes rejected. Second is an alternative hypothesis (HI), which depicts a link between variables ( Mourougan & Sethuraman, 2017) . For instance, “The high cases of chronic diseases are caused by smoking." Upon the development of a hypothesis, the researcher goes further to test the hypothesis. Data collected from the experimental group is used to determine whether the condition under investigation impacts the outcome. Once data is collected and tested, analysis and conclusion are done, which either accepts or rejects the null hypothesis.
When analyzing data, an appropriate statistical test is essential for accuracy. When selecting an appropriate statistical test, the researcher considers several factors among them the nature the research is dealing with, whether the research is dealing with normal distribution and the objective of the study ( Mishra, Pandey, Singh, Keshri & Sabaretnam, 2019) . Various types of data considered include nominal data, ordinal data where categorical data are ordered logically, interval data, and ratio data. The principal methods used in checking normal distribution include measurement of skewness and kurtosis, plotting Q-Q plot, and plotting histogram. Typically, Spearman or Pearson correlations are the best statistical test for questions interval or ratio-level intervals. A chi-square test is used where the research has a relationship with two categorical variables. A wrong selection of statistical tests leads to confusion and challenges when evaluating the findings and coming up with a conclusion of the study.
Once the necessary data is collected and tested, the researcher needs to go further and evaluate the data to determine its validity. Results derived from statistics are used to analyze and interpret numerical and categorical data. The researcher has to use all data collected to calculate the statistical parameter. When evaluating data, the standard deviation and mean are calculated. Also, P-value is identified to accept or reject the null hypothesis. ANOVA and regression tests are the two primary ways used to evaluate statistical results. ANOVA test is used to ensure the existence of average within each variable test group for easy evaluation and conclusion. The absence of averages is an indicator of error during the analysis. A regression test is used to determine the connection between various variables. Evaluation of statistical data is crucial for the verification of results accuracy to avoid deciding on insufficient data.
In summary, this class has been informative on the importance of analyzing statistics. Statistical analysis is useful in the analysis of current information and the prediction of future outcomes. Business managers rely on statistics to make an informed decision for improving the business. And plan for the future. Apart from business, other fields also require statistical analysis like in medicine, sociology, and politics for better planning. Understanding various elements of statistics helps managers and researchers from all areas to make accurate decisions based on the compiled data.
References
Byrne, G. (2007). A statistical primer: Understanding descriptive and inferential statistics. Evidence-based library and information practice , 2 (1), 32-47.
Lowry, R. (2014). Concepts and applications of inferential statistics.
McCarthy, R. V., McCarthy, M. M., Ceccucci, W., & Halawi, L. (2019). What Do Descriptive Statistics Tell Us. In Applying Predictive Analytics (pp. 57-87). Springer, Cham.
Mishra, P., Pandey, C. M., Singh, U., Keshri, A., & Sabaretnam, M. (2019). Selection of appropriate statistical methods for data analysis. Annals of cardiac anaesthesia , 22 (3), 297.
Mourougan, S., & Sethuraman, K. (2017). Hypothesis Development and Testing. J. Bus. Manag , 19 , 34-40.
