In statistical researches, a description of data and analysis of a hypothesis is so crucial. Additionally, a keen assessment of relationships that occur amongst variables is essential since this will lead to an accurate solution to the study problem. Determining the causes and the effects of variance that exists in variables is essential in making informed changes. This paper will focus on the description of various aspects of statistics in research.
Describing data is essential in research since it shows a summary of patterns that emerge from a given set of data. For simple interpretation of data, it is vital to illustrate the measures of central tendency by determining the median, mean, and mode. Also, one should describe how spread out the data is by calculating the various measures of spread such as variance and standard deviation. It is important to summarise data using tables, graphs, charts, and arithmetical commentary (Fundi et al., 2017; p.1-6). In the natural resource industry, describing data has enhanced predictive modeling to aid in making a decision that has been applied in ingesting and integrating large amounts of data from graphical, geospatial text, and temporal data.
Rees and David (2018; pp139-60) explains that testing a hypothesis enables the experimenter to determine if the observation of a given phenomenon is likely to have actually occurred established on statistics. Additionally, it unravels the differences in groups, associations between variables, and the effects of certain treatments. For instance, if one rejects a null hypothesis, it is proof that the outcome is statistically significant; therefore, coincidental and luck played no role. Contrary, if one fails to reject a null assumption, then it implies that the study had no influence or difference. Medical procedures and pharmaceutical drugs are tested using this method.
Examining a relationship between variables provides a platform for regression that uses the proven correlations between dependent and independent variables to predict the values of the dependent variable. Additionally, correlation matrices play an important role in obtaining factor solutions by studying the construct validity of data (Losh & Susan, 2017; p.1-3). Assessment of relationship is key during the development and testing of theoretical models by precisely explaining the nature of bivariate correlations.
Oyediran et al. (2018; p.1-6) defined a research problem as an exact difficulty, contradiction, issue, or gap in information that one focuses on. There are two types of research problems. First are practical problems that contribute to change. For instance, you can look for processes that could be improved in an organization. Second is theoretical research problems aimed at expanding knowledge or understanding — for instance, investigation on the connection between economic growth, population size, and political status. Mostly the aim of the research should be adhered to throughout the process to realize accurate outcomes.
According to Khan et al. (2019; p. 7-11), causal-comparative is an attempt to find out the consequences or cause of differences among groups of individuals. There exist three types of Causal comparative research; exploration of effects, cause, and consequences. Auditorium testing refers to allowing the target audience to rate your research by use of various ways, including electronic gadgets or questionnaires. Treatment groups are test subjects and variables under study. Analogous is a degree of similarity between two variables. Pseudo-experimental refers to using inferential statistics to ascertain treatment effects with an error term inappropriate to the hypothesis under consideration. It is important to include the control in any experiment for clarity in comparison of results.
In summary, the choice of an appropriate research design is crucial in attaining accurate results for any study problems. All the variables under analysis should be analyzed by the use of relevant tests to unravel their relationships. The most significant part of any research is the ability to present the results clearly for easy understanding.
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Losh, S. C. (2017). Dependent and Independent Variables. The Wiley‐Blackwell Encyclopedia of Social Theory, 1-3. Revised from; https://onlinelibrary.wiley.com/doi/pdf/10.1002/9781118430873.est0622
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