How medical professionals handle data collecting and analysis has changed substantially in recent years as a result of the adoption of information technology in the healthcare industry. Many facets of the work of nurses with a Doctor of Nursing Practice (DNP) degree depend on data. This is true regardless of whether they work as nurse practitioners or in senior positions within healthcare institutions. But the use of data extends much further than just keeping electronic health records (EHRs). Insights are increasingly influencing provider performance, coordinating performance with value-based reimbursement models, optimizing processes in the healthcare system, and directing changes in care delivery. As a result, it is now expected of DNP-prepared nurses to comprehend the relationship between statistical analysis and healthcare and to be at least somewhat proficient in statistics.
Nurses must comprehend the distinction between descriptive statistics and inferential statistics as well as how to apply both approaches to resolve practical issues if they are to be successful in using these types of insights. The American Nurses Association (ANA) states that nurses at all levels should be able to comprehend and use fundamental statistical analyses that are connect to performance improvement initiatives.
Descriptive Statistics: What Are They?
Descriptive statistics are essentially use to record or characterize the qualities or properties of data. They provide quantitative insights on a given numerical data set, or several sets, through numerical or graphical representation.
Only the data to which they are apply to reflect by descriptive statistics. One type of descriptive statistic is:
- The average, median, or mode of a central tendency: These are utilize to locate an average or center point within a set of data
- A metric for variability or dispersion, such as variance, standard deviation, skewness, or range: These display the distribution of the data points
- A distributional metric, such as the amount or percentage of a certain result: These represent the prevalence of that result within a data set
Descriptive statistics can be use to examine practically any quantitative data, including the outcomes of a clinical trial looking at a drug’s negative effects.
According to descriptive statistics that reflect central tendency, the average age of those who tried the medicine was 37. Descriptive statistics could also reveal that 25% of the group reported mild side effects, 2% felt moderate to severe adverse effects, and 73% felt no side effects as a measure of distribution.
Pie charts, line graphs, tables, and other visualizations summarise the information acquired about a particular population. They can be use to portray the raw data as statistics and graphs. To get help with these subjects you can now hire pay someone to write my dissertation services online.
What Are Inferential Statistics?
When drawing conclusions or inferences from the data available from a smaller sample population, inferential statistics are applied. Analyzing a random sample from a much bigger data collection, such as a larger population, is a common way to accomplish this. This sample’s findings are extrapolate to the total population.
To guarantee that the inference drawn is accurate, the relevance and caliber of the sample population are crucial. This holds whether the population consists of a collection of individuals, a region, a set of medical facilities, or something altogether different. A representative sample needs to be sizable enough to yield statistically significant results. But not too sizable that it makes analysis difficult.
Techniques for inferential statistics include:
- Tests of hypotheses or significance: This entails determining if specific results are important and not just a coincidence
- Analysis of correlation: This identifies the relationship or correlation between two variables
- Analyses of linear or logistic regression These techniques make it possible to infer and forecast causation as well as other correlations between variables
- Confidence intervals: These indicate the likelihood that an estimated event will materialize
In a study on comorbidities, for instance, inferential statistics may be apply. Researchers can examine a small group of patients with share characteristics, such as those with more than two chronic diseases, and extrapolate results across the larger population from which the sample was selected, as oppose to scouring extensive healthcare data in their entirety.
It is possible to identify correlations, probabilities, and other linkages in the patient data by examining a random sample of patients within this population. The activities or policy decisions made by providers to improve patient care across the population may be influence by these findings.
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What Sets Descriptive Statistics Apart from Inferential Statistics?
The main distinction between descriptive and inferential statistics is that inferential statistics are use to conclude a larger population, but descriptive statistics are not. Inferential statistics are use to produce insights across enormous data sets that would be difficult or impossible to evaluate, rather than to report on the data set itself.
Descriptive statistics, on the other hand, present facts and results that have been verified from a population, whereas inferential statistics examine samples to make inferences about larger populations.
The percentage breakdown of side effect frequency and mean age in the clinical drug trial example are statistical metrics of central tendency and normal distribution within that data set (Surbhi S, 2019)
To make inferences about how these side effects affect people using this medicine, inferential statistical approaches could be used. Based on how many people in the sample population had that side effect, the resultant inferential statistics can assist physicians and patients in determining how likely it is that they will have it.
The certainty level is quite high since descriptive statistics concentrate on the properties of a data set. To ensure more accuracy, outliers and other factors may be eliminated from the overall findings. But calculations are frequently considerably simpler and can produce reliable results.
However, inferential statistics, which may include several variables, are intend to test for a dependent variable, meaning the population characteristic or outcome under study. Although the computations are more sophisticated, the outcomes are less certain. There will also be room for error. Inferential statistics, after all, are more akin to highly educated hypotheses than claims. Although several statistical techniques can be use to reduce undesirable outcomes, a sampling mistake may skew the results.