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Introduction
Statistics is a mathematical representation of analyzed data. This method can be applied in almost all areas to explore past, present, and future performance. Statistical data can compare and contrast records that may be used for a specific future event. Collected material can predict the likely happening of a particular event. A small sample group is usually selected to represent the entire region in collecting data on a population. Collected material will then be analyzed, and results will be assumed as overall representation. Analyzed data may serve two purposes; descriptive and inference.
Descriptive
Descriptive statistics summarizes data either numerically or graphically. Examples of numerical representation of statistical information include mean, median, standard deviation, height, percentage, and frequency (Benedict, 2008). The numerical model mentioned above can also be represented graphically. Graphs are mainly used to compare two related variables. One variable is placed on the X-axis and the other on the Y-axis.
Inferential data
Inferential Data is an accounting procedure used to account for data in a random manner. A group is randomly selected from a large population to answer some specific questions. It may be in the form of a questionnaire that requires a designated individual to respond by ‘yes’ or ‘no.’ Answers given by a sample group will provide information about the entire group. Other methods used to collect data include; estimation, correlation, regression, extrapolation, interpolation, data mining, ANOVA, and time series.
Statistical data is a representation that relies on estimates, and it may not give an exact figure about the analyzed population. However, it provides guidance that may facilitate the distribution of resources. Data is based on estimates collected from a small group of individuals (Moore, 2001). Statisticians rely on experiments and the information gathered from fields to make assumptions. Most statistical data is therefore exaggerated and may not be relied upon. Interpretation of some statistical data may be complex to an ordinary person and require technical assistance. It may also be interpreted from different angles by the two observers, which makes it unreliable for public use.
The goal of statistical methods is to provide needed information that may be used to identify a specific event for the development of knowledge. The information gathered helps the concerned groups know the state of affairs in a particular situation (Benedict, 2008). For example, suppose a specific organization desires to implement development projects in a specific region. It may be difficult for them to know the area in urgent need of such a facility. Therefore, they will rely on statistical data collected to study the distribution of such facilities. A place with limited resources will be considered for their project.
Statistical data ensures that resources are adequately distributed, eliminating bias. The government and nongovernmental organizations mostly use it to facilitate the distribution of essential resources such as health care facilities, learning institutions, food, and other charitable services in times of calamity and social amenities (Moore, 2001). Organizations will compare various variables they believe will make a particular group be considered more than the other. Statistical methods may also be used to predict the occurrence of a specific event to prepare and take cover in advance. The events may be positive or negative where business organizations use statistics to indicate the economic trend, while health care organizations may use them to predict the outbreak of any disease and take preventive measures.
References
Benedict C. (2008): Statistical Methods: BiblioBazaar LLC, South Carolina pp 32-40.
Moore S. (2001): Statistics: concepts and controversies: W. H. Freeman, New York pp 24-30.
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