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Difference between Descriptive and Inferential Statistics

Difference between Descriptive and Inferential Statistics.

 Reply 1

“Name two different methods for evaluating evidence. Compare and contrast these two methods”

When researching there is a common course from the time of research to evaluating the results of the research. Two different types of data analysis within the statistical realm are Inferential and descriptive statistics. Both are a way to interpret scientific evidence, but have differing methods of reporting the evidence.

Descriptive Statistics

Descriptive statistics allows the researcher to take a sample group and graph the data to understand that specific set of observations from only the information they have witnessed to describe the results (Frost, 2021). This type of research analysis allows one to achieve more insight and visualization of the results of only what has been studied or seen, instead of assuming information about a larger population (Frost, 2021).

Inferential Statistics

Inferential statistics uses data from a small sample and makes inferences from them, applying the results to a larger population; due to this results must be extremely accurate (Frost, 2021). With this type of sampling there is a margin for error because if the sample does not accurately reflect the population being researched it will skew the results (Frost, 2021).

While both types of research data analyze research results, descriptive statistics will be more accurate since it is only reporting data true to its research and sample size, and nothing outside of that. On the other hand, inferential statistics applies results from a smaller sample size to a bigger population, which may produce variations in results.


Frost, J. (2021). Difference between Descriptive and Inferential Statistics [Webpage]. Statistics by Jim. Retrieved June 2, 2021, from https://statisticsbyjim.com/basics/descriptive-inferential-statistics/

Reply 2

, 2021 07:07 PM0 Like Profile PictureRobert Sapio 3 postsRe: Topic 3 DQ 2

There are several methods of evaluating evidence methods, but the two most common methods of evaluating evidence are Meta-Analyses and Systematic Review. A systematic review are literature reviews that summarize evidence by identifying, selecting, assessing, and synthesizing the findings of similar but separate studies (Agency for Healthcare Research and Quality [AHRQ], 2018). Meta-analysis is the combined form of many data from different studies. The systemic review is a meticulous type of research whereas the meta-analysis is a statistical process which combines studies results and test the hypotheses. The systemic review is also known as secondary research and meta-analyses is the process of systemic research. A systematic review summarizes the results of available carefully designed healthcare studies (controlled trials) and provides a high level of evidence on the effectiveness of healthcare interventions” (Cochrane Consumer Network, n.d.,). This type of review is regarded as the best research evidence (Helbig, J. 2018).

The critical appraisal is used to identify the strengths and weaknesses of the article selected for the literature review. When evidence is gathered, the researcher performs a critical appraisal of the “study to ensure its credibility and clinical significance. It is important to determine not only what was done, but how well it was done” (Connor, 2014).

Both the Systematic Reviews and Meta-Analyses are considered the highest quality of evidence for clinical decision making and can be used above all the other methods of evaluating evidence. Both methods for evaluating evidence are similar because they involve the collection of data from different sources and summarizing all the evidence and results of the studies.

While systematic review collects and summarizes all the empirical evidence, the meta-analysis uses statistical methods to summarize the results of the studies. Meta-analysis is a statistical method used to combine the numerical results from such studies if it is possible to do. On the other hand, systematic review is a formal, systematic, and structured approach to review all the relevant literature on a topic. The other difference is, the rationale for Meta-analysis is that through the combination of samples from different studies the overall sample size is increased, while the rationale for systematic reviews is that when data is combined from different sources a greater reliability would be gained.

When performing a systematic literature review or meta-analysis, and the quality of studies is not rigorously evaluated or if proper methodology is not strictly applied, the results can be biased, and the outcomes can be incorrect. However, when systematic reviews and meta-analyses are properly implemented, they can yield powerful results.


Agency for Healthcare Research and Quality. (2018). National guideline clearinghouse: Fact sheet. Retrieved from https://www.ahrq.gov/research/findings/factsheets/errors-safety/ngc/national-guideline-clearinghouse.html

Cochrane Consumer Network. (n.d.). What is a systematic review? Retrieved from http://consumers.cochrane.org/what-systematic-review

Connor, B. T., (2014). Differentiating research, evidence-based practice, and quality improvement. Retrieved from https://www.americannursetoday.com/differentiating-research-evidence-based-practice-and-quality-improvement/

Helbig, J. (2018) Nursing Research: Understanding Research for Best Practice. History and Process of Nursing Research, Evidence-Based Nursing Practice, and Quantitative and Qualitative Research Process. Retrieved from: https://lc.gcumedia.com/nrs433v/nursing-research-understanding-methods-for-best-practice/v1.1/#/chapter/1

Difference between Descriptive and Inferential Statistics

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