Difference between Descriptive and Inferential statistics

The key difference between descriptive and inferential statistics is descriptive statistics aren’t used to make an inference about a broader population, whereas inferential statistics are used for this purpose. Rather than being used to report on the data set itself, inferential statistics are used to generate insights across vast data sets that would be difficult or impossible to analyze. People use descriptive statistics to repurpose hard-to-understand quantitative descriptive vs inferential statistics insights across a large data set into bite-sized descriptions.

This is true whether the population is a group of people, geographic areas, health care facilities, or something else entirely. A representative sample must be large enough to result in statistically significant findings, but not so large it’s impossible to analyze. In recapping a Major League Baseball season, for example, descriptive statistics might include team batting averages, the number of runs allowed per team, and the average wins per division. The preparation and reporting of financial statements is an example of descriptive statistics. Analyzing that financial information to make decisions on the future is inferential statistics. Bivariate data, on the other hand, attempts to link two variables by searching for correlation.

Step 3. Conduct a power analysis to determine a sample size

Descriptive statistics have a different function from inferential statistics, which are data sets that are used to make decisions or apply characteristics from one data set to another. They divide the data into bins or intervals and represent the frequency or count of data points falling into each bin through bars of varying heights. Histograms help identify the shape of the distribution, central tendency, and variability of the data. The median is the best measure of central tendency from among the mean, median, and mode.

Foundational statistical techniques

If, for every pair of values of a variable, a value exactly mid-way between them is meaningful, the variable is continuous. For example, two values for the time taken for a weal to subside can be 10 and 13 min. However, for a number of weals, suppose you have a pair of values – 5 and 8 – the midway value would be 6.5 weals, which does not make sense. Discrete variables are variables in which no values may be assumed between the two given values (e.g., number of lesions in each patient in a sample of patients with urticaria). Let’s return to our previous example with the box of chocolates to see how inferential statistics work. Inferential statistics is crucial for coming up with conclusions or testing ideas, especially when it wouldn’t be practical or even possible to study those ideas, due to the scale and scope involved.

1. For nurses who hold a Doctor of Nursing Practice (DNP) degree, many aspects of their work depend on data.
2. Measures of variability (or measures of spread) aid in analyzing how dispersed the distribution is for a set of data.
3. These two measures use graphs, tables, and general discussions to help people understand the meaning of the analyzed data.
4. Typical examples of descriptive statistics can include mean, mode, and frequency tables.
5. It gathers the same sales data above, but it uses the information to make predictions about what the sales of the new hot sauce will be.

Led by seasoned instructors, our curriculum is enriched with hands-on projects, real-world simulations, and case studies, fostering a practical learning environment essential for your triumph. Through our courses, you’ll master the art of data analysis, adeptly craft insightful reports, and harness the power of data-driven decision-making pivotal for steering business triumphs. Methods to collect evidence, plan changes for the transformation of practice, and evaluate quality improvement methods will be discussed. The raw data can be represented as statistics and graphs, using visualizations like pie charts, line graphs, tables, and other representations summarizing the data gathered about a given population. In recent years, the embrace of information technology in the health care field has significantly changed how medical professionals approach data collection and analysis. For nurses who hold a Doctor of Nursing Practice (DNP) degree, many aspects of their work depend on data.

Types of Statistics:

The presence of outliers can have a notable impact on descriptive statistics, skewing results and affecting the interpretation of data. Outliers can disproportionately influence measures of central tendency, such as the mean, pulling it towards their extreme values. For example, the dataset of (1, 1, 1, 997) is 250, even though that is hardly representative of the dataset. This distortion can lead to misleading conclusions about the typical behavior of the dataset. Descriptive statistics help describe and explain the features of a specific data set by giving short summaries about the sample and measures of the data. For example, the mean, median, and mode, which are used at almost all levels of math and statistics, are used to define and describe a data set.

Then, using data analytics, we mathematically or graphically depict whether there is a relationship between student age and test scores. Basically if the distribution curve is symmetric, it looks the same on either side of the central point. Distributions with high kurtosis tend to have “heavy tails” indicating a larger number of outliers, whereas distributions with low kurtosis have light tails, indicating lesser outliers. Continuous variables, on the other hand, can take any value in between the two given values (e.g., duration for which the weals last in the same sample of patients with urticaria). One way of differentiating between continuous and discrete variables is to use the “mid-way” test.

What are the Tools Used in Descriptive and Inferential Statistics?

We also need to know that broadly sampling can be divided into two types – probability sampling and nonprobability sampling. The most common tools used for visual display include frequency diagrams, bar charts (for noncontinuous variables) and histograms (for continuous variables). For example, the frequency distribution in a sample population of males and females can be illustrated as given in Figure 1. 1,2 To put it in very simple terms, a variable is an entity whose value varies.

Therefore, it will likely be helpful for researchers to include a biostatistician as early as possible in the research team when designing a study. The tools used in descriptive and inferential statistics are measures of central tendency, measures of dispersion, hypothesis testing, and regression analysis. The three main types of descriptive statistics are frequency distribution, central tendency, and variability of a data set. The frequency distribution records how often data occurs, central tendency records the data’s center point of distribution, and variability of a data set records its degree of dispersion.

Variance of data is calculated by dividing the sum of the squares of each data point’s differences from the data’s average by the total number of values in the data. Interquartile Range(IQR) is defined as the difference between the third quartile (Q3) and the first quartile (Q1), and it is less affected by the outliers. IQR is the range for the middle 50% of the observations in the data as it is calculated after removing the highest and the lowest 25% of observations in a dataset after arranging them in ascending order. The most important measures of location or central tendency are mean, median, and mode. In the next section, let’s drill down on each type of descriptive statistics.

1. The difference between each observation from the mean is called the deviation of the mean.
2. The frequency distribution records how often data occurs, central tendency records the data’s center point of distribution, and variability of a data set records its degree of dispersion.
3. It is important to calculate the sample size much in advance, rather than have to go for post hoc analysis.
4. The methods of inferential statistics are (1) the estimation of parameter(s) and (2) testing of statistical hypotheses.

In the example of a clinical drug trial, the percentage breakdown of side effect frequency and the mean age represents statistical measures of central tendency and normal distribution within that data set. As an example, inferential statistics may be used in research about instances of comorbidities. Inferential statistics are used to make conclusions, or inferences, based on the available data from a smaller sample population. This is often done by analyzing a random sampling from a much broader data set, like a larger population. For nurses to succeed in leveraging these types of insights, it’s crucial to understand the difference between descriptive statistics vs. inferential statistics and how to use both techniques to solve real-world problems.