Understanding Descriptive and Inferential Statistics in Nursing Research

Understanding Descriptive and Inferential Statistics in Nursing Research

Differences between Descriptive and Inferential Statistics

Within the field of numerical analysis, there are two branches: descriptive statistics and inferential statistics. These branches differ in their research functions.

Descriptive Statistics

Descriptive statistics refers to the data techniques that outline the datasets and their features. These variables can be the measures of center (mean, mode, and median) and of dispersion (range, variance, and standard deviation) (Al-Benna et al.  2010). The purpose of descriptive statistics is to inform us about the important features of data that enable us to understand its distribution and this new information. In brief, descriptive statistics are a type of statistics that arrange and summarize complicated datasets, which researchers can, thus, gather the main findings of their study.

Inferential Statistics

An inference statistic can be used for making estimates and drawing conclusions about a population by using sample data. Also, inferential statistics make it possible to determine the features of the population the sample was derived from. This is the method that uses the theory of probability to calculate the parameters and verify the hypotheses. Inferential statistics include methods of hypothesis testing, confidence intervals, and regression analysis that allow for the analysis of the relationships between variables and make predictions about forthcoming outcomes (Guetterman, 2019).

Probability Sampling and Sample Size in Inferential Statistics

Random sampling of the population ensures that every member has the same chance of selection; hence, the sample becomes more representative of the population. Thus, this technique eliminates the selection bias, and researchers can, therefore, more accurately conclude the whole population. In addition, a bigger sample size also increases the statistical precision because of the reduction of the variability, and hence, the chance of finding the significant effects is greater. In inferential statistics, random sampling is the key that enables researchers to estimate population parameters with much higher accuracy, while a bigger sample size makes the statistical power stronger (Kaliyadan & Kulkarni, 2019). The larger the statistical power of the researchers, the more confident they will be in the population inferences from the data of the sample. Therefore, both the random sampling and the sample size are the most important aspects of inferential statistics because they determine how the research results can be generalized to the whole population (replicability, reliability), and at the same time, they are the two things that will guarantee the validity and the reliability of the study results.

Interpreting the P-Value

The p-value illustrates the probability of getting the observed outcome or even more extreme results when the null hypothesis is true. Thus, it measures the power of the evidence which is against the null hypothesis. A smaller p-value indicates strong evidence against the null hypothesis, which means that the observed results are not likely to happen by chance (Feng et al., 2022). This is very important to evidence-based practice because it enables clinicians and researchers to evaluate the relevance of their findings. A low p-value shows that the observed effect is statistically significant; thus, it is not possible that it happened by accident. Hence, healthcare decisions and interventions based on these findings are more likely to be effective and trustworthy. P-values are a standard method to evaluate the strength of evidence in research, thus helping practitioners make the right decisions in the field of patient care and treatment.

Causation vs. Correlation

Causation is the connection between cause and effect, where one variable is directly responsible for the other. On the contrary, correlation means a statistical relationship between two variables, where a change in one variable is connected with a change in another variable; nevertheless, one of them does not necessarily cause the other (Feng et al., 2022). Correlation is not the same as causation since other factors might be the reason behind the correlation.

Correlational measurements like correlation coefficients are used to show the extent and the type of the relation between the variables. There is a positive correlation, which means that the increase of one variable will increase the other variable. For example, the higher the patient satisfaction scores, the greater the possibility that the patients will take their drugs as prescribed by their doctors. However, a negative relationship means that the measure of one variable usually declines when the measure of the other variable rises. For example, the high patient satisfaction scores can be the reason for the negative correlation between hospital readmission rates.

References

Al-Benna, S., Al-Ajam, Y., Way, B., & Steinstraesser, L. (2010). Descriptive and inferential statistical methods used in burns research. Burns, 36(3), 343–346. https://doi.org/10.1016/j.burns.2009.04.030

Feng, G., Qin, G., Zhang, T., Chen, Z., & Zhao, Y. (2022). Common Statistical Methods and Reporting of Results in Medical Research. Cardiovascular Innovations and Applications, 6(3), 117–125. https://doi.org/10.15212/cvia.2022.0001

Guetterman, T. C. (2019). Basics of statistics for primary care research. Family Medicine and Community Health, 7(2). https://doi.org/10.1136/fmch-2018-000067

Kaliyadan, F., & Kulkarni, V. (2019). Types of variables, descriptive statistics, and sample size. Indian Dermatology Online Journal, 10(1), 82–86. https://doi.org/10.4103/468_18

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 The purpose of statistics is to generate meaning from the data so that researchers can draw conclusions and make recommendations for nursing practice. There are two classifications of statistics; they are descriptive and inferential statistics. Descriptive statistics describes and organizes data whereas inferential statistics makes inferences about a population.

Understanding Descriptive and Inferential Statistics in Nursing Research

Instructions
1. Review the rubric to make sure you understand the criteria for earning your grade.
2. Read Chapters 15 and 16 in your textbook: Foundations of Nursing Research.
3. Review the article, Sample size in quantitative research: Sample size will affect the significance of your research e.
4. View the Chapter 15 and Chapter 16 PowerPoint files.
5. Prepare to discuss the following Discussion Prompts:
a. Describe the differences between descriptive and inferential statistics.
b. Describe how probability (random) sampling and sample size relate to inferential statistics and the ability to generalize the findings to a larger population.
c. Discuss what a p-value indicates about the results. Why is this important to evidence-based practice?
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d. Describe the difference between causation and correlation. How does a correlational measurement demonstrate the strength of the relationship between variables? Provide examples of both a negative and a positive correlation, using patient satisfaction scores as one of the variables.
6. Find at least two current scholarly sources to support your explanations and insights. OCLS resources are preferred sources and can be accessed through IWU Resources. Wikipedia is not permitted, as it is not a peer-reviewed, scholarly source.
7. Whether written or spoken, interactions are expected to:
a. clearly and thoroughly address the prompt with meaningful information that shows critical thinking.
b. introduce your own ideas and questions to add greater depth to the discussion, rather than restating what your classmates have shared. (Include much more than “Great post,” or “I agree.”)
c. refer to relevant course concepts as you discuss your learning together.
d develop insightful conversation by directly addressing your placemat! ideas