Descriptive and inferential statistics are considered essential tools for data analysis and interpretation, and researchers often use them to understand the complete picture of the research topic. I have used both descriptive and inferential statistics in my project management work, more descriptive statistics and less inferential statistics.
Descriptive statistics
According to Clippinger (2017), This definition refers to one of the statistical tools concerned with describing, summarizing, and measuring the actual data, which reflects reality without making any assumptions. Accordingly, a decision can be reached using the results of these statistics and determining any abnormal values in them by calculating measures of variance, measurements of shape, central tendency, and others (Proches, 2016). Among the descriptive statistics tools are: (Histograms - Box plots - Bar charts - Line charts - Scatter plots).
Inferential statistics
According to Marshall & Jonker (2011), This definition refers to one of the statistical tools concerned with describing, summarizing, and measuring data assumed as a result of random sampling. These tools direct researchers to estimate the level of uncertainty and confidence interval associated with the results obtained from these samples. Among Inferential statistics tools:(are hypothesistesting, T-tests, Analysis of variance - ANOVA, Regression analysis, and Chi-square tests).
Using Descriptive statistics & Inferential statistics
Descriptive statistics display research results of various kinds, such as medical and social research and others, as well as surveys and participants' responses (Laerd Statistics, 2018). They can also be used in quality reports and performance monitoring. On the other hand, Inferential Statistics is used to test hypotheses about the behavior of large groups of people., evaluate treatment and hospitalization programs, the effects of treatment, and medical drugs.
The apparent difference between them is that descriptive statistics describe what is known and realistic about a sample and do not transcend the data to any assumptions. Inferential Statistics uses predictions and assumptions and estimates the level of uncertainty associated with these assumptions. Therefore, inferential statistics can be used on a broader scale than descriptive statistics, as inferential statistics can be applied to a more significant population segment (Amrhein, Trafimow & Greenland, 2019).
Conclusion
Both Descriptive statistics and inferential statistics are essential tools in the research process. Through them, research results can be read more quickly than if formulated in writing only. Also, modern technology significantly impacts these tools, as statistical programs are used to obtain quick and correct results better than the old manual methods. The practicing researcher must use these tools correctly. Otherwise, the results will be misleading, inaccurate, and contrary to reality.
References
Amrhein, V., Trafimow, D., & Greenland, S. (2019). Inferential statistics as descriptive statistics: There is no replication crisis if we don’t expect replication. The American Statistician, 73(sup1), 262-270.
Clippinger, D. (2017). Descriptive and Inferential Statistics in Business Research Reporting. Business Expert Press. Retrieved from Ebook Central, (Read pages 62-70)
Laerd Statistics. (2018). Descriptive and inferential statistics. Retrieved from https://statistics.laerd.com/statistical-guides/descriptive-inferential-statistics.php
Marshall, G., & Jonker, L. (2011). An introduction to inferential statistics: A review and practical guide. Radiography, 17(1), e1-e6.
Proches, S. (2016). Descriptive statistics in research and teaching: Are we losing the middle ground? Quality and Quantity, 50(5), 2165-2174. Retrieved from ProQuest One Academic.