Descriptive statistics is used to describe basic features of the data presented in a study. As such, the statistics is aimed at providing summaries about the measures and data used within the study. Inferential statistics, on the other hand, aim to reach at conclusions presented by the data. As such, an extrapolation of the data presented in the study is done to reach at logical conclusions that could come about. For example, an inference can be made from a sample data on what the population could think on a certain phenomenon. This paper looks into descriptive and inferential statistics when engaging quantitative studies.
Notably, quantitative studies engage the study population for their opinion on certain issues. For example, a quantitative study will look to find out the attitudes of a sample population towards a certain phenomenon. This could be a wide variety of things, including the application of the law, the viability of sex education laws among other things. Therefore, the quantitative study will engage the direct views of the sample population on the issue. However, qualitative studies need not engage the sample population as they look to analyze existing literature on the subject matter of the research. Therefore, qualitative studies do not face the challenge of the application of inferential or descriptive statistics as they depend on already-synthesized information from other scholars in their studies. Moreover, qualitative studies will rarely engage the attitudes of peoples directly as they concentrate on analyzing existing data and inferences from the same.
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Quantitative studies, however, will often face this challenge as they include the direct opinion of persons within the study known as the sample population. Of necessity then, will be to make a description of certain aspects of the population, including the demographic factors as well as make the inferential analysis of the data (Trochin, 2006). For example, if the study comprised of a larger proportion of women, one could infer that women preferred (or didn’t prefer) the item of the subject matter as an inference to the study results. The simple statement that women were more than men (having compared this in percentages) amounts to the descriptive part of the sample population statistic.
In reporting statistics in general, it is important to understand the significance of the results that one gets. While in layman English this may mean important, statistical significance does not necessarily mean that the findings could be important. The p value, which represents significance in statistics, determines whether differences that are observed between sample groups are due to chance or could be considered to be “real” (Institute for Work & Health, 2005). This means that the results, should they be applied in a different environment, could achieve similar results. Such a study would go into determining whether, if by chance, the same results in a group A would occur without applying same circumstances in a group B.
Understanding statistical significance is key in ensuring that the study results can be trusted and applied over a number of variant circumstances evaluating the same subject matter. The research finding with higher statistical significance is more applicable to variant situations as opposed to lower statistical significance situations. At statistical significance, it is assumed that higher statistical significance is closer to the actual situation because the weighted averages of both items in testing are assumed to start off as like items. Therefore, the higher difference is assumed to become the more significant.
References
Institute for Work & Health. (2005). What researchers mean by... statistical significance . Retrieved October 3, 2016, from Institute for Work & Health: https://www.iwh.on.ca/wrmb/statistical-significance .
Trochin, W. (2006). Descriptive Statistics . Retrieved October 3, 2015, from Research Methods Knowledge Base: http://www.socialresearchmethods.net/kb/statdesc.php .
