PUB 550 Discuss why both descriptive and inferential statistics are used in the analysis of public health data

PUB 550 Discuss why both descriptive and inferential statistics are used in the analysis of public health data

PUB 550 Discuss why both descriptive and inferential statistics are used in the analysis of public health data

Descriptive Statistics is a branch of statistics that indicates to provides information and defines the data briefly. Descriptive Statistics refers to a discipline that quantitatively describes the data. Descriptive statistics describe only particular people or items that are measured. It is applied widely for small projects but the results cannot be extrapolated to a large population. In descriptive statistics, we can take a sample according to our interests and analyze data to present its properties. Descriptive statistics are used to describe a particular situation. It gives details of the data that is recognized and summarizes the data of the sample. Descriptive statistics mostly use statistical measures like Central tendency, dispersion, and skewness.

Inferential Statistics is a type of statistics that defines data of a larger population by taking a small portion of that population and drawing a conclusion from it. Inferential Statistics refers to a discipline that provides information and draws a conclusion about a large population from the sample of it. Inferential statistics describe data about the population entirely. It is more applicable for larger data set projects. In Inferential statistics, a sample is done through different forms of sampling. Inferential statistics are used to clarify the probabilities of occurrence of an event. It attempts to reach the conclusion to learn about the population that extends beyond the available data. Common statistical tools of inferential statistics are hypothesis Tests, confidence intervals, and regression analysis.



Corty, E. W. (2016). Using and interpreting statistics (3rd ed.). Worth Publishers.

Wagle, K. (2020, November 30). Descriptive statistics vs inferential statistics- 8 differences. Public Health Notes. Retrieved July 7, 2022, from 


Brief descriptive coefficients, known as descriptive statistics, are used to sum up a specific data set, which may be a sample of a population or a representation of the whole population. Measurements of central tendency and measures of variable spread make up descriptive statistics.

When comparing the descriptive statistics with the inferential statistics, we can see that they are self-explanatory since when we refer to the descriptive one, it allows us to see how the information that has already been collected is explained in the forms of graphic tables. If we look for what these two have in common, we can say that they both use information already given. Besides that, inferential statistics is based on reaching conclusions about the population that is in your database and outside of it.

Another difference is that descriptive is that it calculates the definitive measurement, while in inferential it observes the margin of error of the investigations already carried out. The reason why they are important for public health is that it helps to analyze situations and investigate further in order to identify solutions or conclusions.



Descriptive and Inferential Statistics. (2018). Retrieved from.


  1. Bundly. Descriptive & Inferential Statistics: Definition, Differences & Examples. Retrieved from.

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According to statology, descriptive statistics aims to describe a chunk of raw data using summary statistics, graphs,

PUB 550 Discuss why both descriptive and inferential statistics are used in the analysis of public health data
PUB 550 Discuss why both descriptive and inferential statistics are used in the analysis of public health data

and tables. Descriptive statistics are useful because they allow you to understand a data group more quickly and efficiently than just staring at rows and rows of raw data values (statology, 2020). There are three common forms of descriptive statistics: summary statistics, graphs, and tables. Summary statistics summarize data using a single number; a graph provides visualization for data using boxplots, histograms, etc. A table provides information on how data is distributed. Descriptive statistics are measures of central tendency and include the mean, median, and mode. Inferential statistics uses a small sample of data to draw inferences about the larger population that the sample came from (statology, 2020). Inferential statistics want to answer questions about a population, so data must be collected from a small sample of that population and use the information from the sample to draw inferences about the population. Inferential statistics examine the differences among groups and the relationships among variables. Descriptive and inferential statistics are used in the analysis of public health data to address research questions. Descriptive statistics give a general sense of trends and can illuminate errors by reviewing frequencies, minimums, and maximums that can indicate values outside the accepted range (NIH, 2019). Inferential statistics allow public health workers to understand the likelihood of an outbreak and its side effects based on how many members of the sample population experienced it (NIH, 2019).


The National Library of Medicine. March 2019. Basics of Statistics for Primary Care Research. Family Medicine and Community Health,, 10.1136/fmch-2018-000067.


Statology, 15 Jan. 2020, Descriptive vs. Inferential Statistics: What’s the Difference?

The use of inferential statistics has two major drawbacks. The first and most significant constraint of any inferential statistics is that you are providing data about a population that you have not fully measured, hence you can never be certain that the values/statistics you generate are right. Remember, inferential statistics are based on the concept of using the values measured in a sample to estimate/infer the values that would be measured in a population; there will always be a degree of uncertainty in doing this. The second limitation is connected with the first limitation. Some, but not all, inferential tests require the user (i.e., you) to make educated guesses (based on theory) to run the inferential tests. Again, there will be some uncertainty in this process, which will have repercussions on the certainty of the results of some inferential statistics.



Inferential Statistics: Definition, Uses – Statistics How To. (2021, June 1). Statistics How To.

Descriptive statistics is a term given to the analysis of data that helps to describe, show and summarize data in a meaningful way. It is a simple way to describe our data. Descriptive statistics is very important to present our raw data in an effective/meaningful way using numerical calculations or graphs or tables.

In inferential statistics predictions are made by taking any group of data in which you are interested. It can be defined as a random sample of data taken from a population to describe and make inference about the population. Any group of data which includes all the data you are interested in is known as population.

“Descriptive statistics allow us to examine trends limited to typical values, spread of values and distributions of data. Inferential statistics can help researchers draw conclusions from a sample to a population. We can use inferential statistics to examine differences among groups and the relationships among variables.”(Guetterman, 2019)



Guetterman TC. Basics of statistics for primary care research. Fam Med Community Health. 2019 May;7(2):e000067. doi: 10.1136/fmch-2018-000067. Epub 2019 Mar 28. PMID: 31218217; PMCID: PMC6583801.

Descriptive statistics is the type of statistics that probably springs to most people’s minds when they hear the word “statistics.” In this branch of statistics, the goal is to describe. Numerical measures are used to tell about features of a set of data. There are a number of items that belong in this portion of statistics, such as:

  • The average, or measure of the center of a data set, consisting of the mean, median, mode, or midrange
  • The spread of a data set, which can be measured with the range or standard deviation
  • Overall descriptions of data such as the five number summary
  • Measurements such as skewness and kurtosis
  • The exploration of relationships and correlation between paired data
  • The presentation of statistical results in graphical form


These measures are important and useful because they allow scientists to see patterns among data, and thus to make sense of that data. Descriptive statistics can only be used to describe the population or data set under study: The results cannot be generalized to any other group or population.


Inferential statistics are produced through complex mathematical calculations that allow scientists to infer trends about a larger population based on a study of a sample taken from it. Scientists use inferential statistics to examine the relationships between variables within a sample and then make generalizations or predictions about how those variables will relate to a larger population.


It is usually impossible to examine each member of the population individually. So scientists choose a representative subset of the population, called a statistical sample, and from this analysis, they are able to say something about the population from which the sample came. There are two major divisions of inferential statistics:

  • A confidence interval gives a range of values for an unknown parameter of the population by measuring a statistical sample. This is expressed in terms of an interval and the degree of confidence that the parameter is within the interval.
  • Tests of significance or hypothesis testing where scientists make a claim about the population by analyzing a statistical sample. By design, there is some uncertainty in this process. This can be expressed in terms of a level of significance.