Reflection: What have you learned about statistics?

The Importance of Statistics
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The Importance of Statistics
Introduction
Statistics play an important role in ensuring that data can be collected and analyzed in productive ways to formulate practical conclusions. Such conclusions help most individuals, organizations and institutions make critical decisions (McGregor, 2017). Consequently, as students, it is essential to understand statistics to determine the type of data to be collected and eventually interpret it correctly. Although statistics employ various processes to make conclusions under particular researches appropriately, these processes form the basis for globally accepted standards in the field.
Elements of statistics
Descriptive and Inferential Statistics
Descriptive statistics describe the data analyzed in a meaningful way that presents the information to show any patterns or trends. With large groups of data, visualizing becomes difficult due to the overflowing data, hence descriptive statistics provide a method of simplifying the interpretation of such data (McGregor, 2017). For instance, data can be visualized through graphs, tables and charts that capture the generalized data. The statistics used to descriptive statistics include measures of central tendency to describe a central position whenever data in a frequency distribution is analyzed. Measures of central tendency include mean, mode and median that provides the central position of the data. Apart from that, measures of spread are also used as a form of statistic in the analysis of descriptive statistics. In this case, these measures help analyze how spread out the data is across a frequency distribution. Such measures include standard deviation, variance, quartiles, and range, showing how high or low the data is spread.
On the other hand, inferential statistics are used to draw inferences from the sample taken into consideration by applying it to the larger population under study. Using these types of statistics, accuracy becomes critical since the sample should apply to the population, hence the scientific application of the information (McGregor, 2017). In this case, a representative sample of the population must be taken using scientific calculations to ensure that the research can be replicated and yield similar results. Inferential statistics methodologies include analyzing the data using hypothesis testing and regression analysis used to provide values to summarize the data. Consequently, the essential aspect of inferential statistics lies in its ability to examine a population using a sample, especially when considering each member of the population may be impossible or unfeasible.
Hypothesis Development and Testing
A hypothesis statement in research represents the speculations of the outcome in which the researcher expects. Thus, the hypothesis presents the aim of the study, thus aids in guiding the research towards achieving that goal (Mourougan & Sethuraman, 2017). Therefore, it contains the set of facts in the study that can be further tested to enhance the investigation. Thus, the hypothesis must be simple and easily interpreted, state the expected relationships between variables, and must be testable to support or disapprove ...