It is feasible to utilise both descriptive and inferential statistics when analysing data, such as the number of automobiles sold by 20 sales assistants in an automobile company. You will typically use both descriptive and inferential statistics to evaluate your findings and draw inferences. So, the question arises what do descriptive and inferential statistics mean? And what sets them apart? This article by expert researchers of Affordable Dissertation UK will tell you about inferential versus descriptive statistics and their appropriate usage.
Inferential Versus Descriptive Statistics: Definitions
The properties or qualities of a dataset are described using descriptive statistics. Both simple quantitative observations and the broader procedure of drawing conclusions from these data can be referred to as descriptive statistics. Descriptive statistics can be used to describe a population as a whole or a specific sample. Descriptive statistics are only explanatory; therefore, they are not overly concerned with the distinctions between the two categories of data.
Descriptive statistics aid in the meaningful description, presentation, or summarisation of data so that, for instance, patterns may surface in the data. But descriptive statistics do not let us draw any inferences from the data we have examined or come to any conclusions about any potential hypotheses. It only serves as a means for describing our data.
If we just display our original data, it would be difficult to understand what the data implies, especially if there is a lot of it. It is the reason descriptive statistics is crucial since it enables a more understandable presentation of the data, making data interpretation easier. Distribution and measures of central tendency are crucial concepts which are defined as follows:
Distribution in Descriptive Statistics
In a population or sample, distribution reveals the frequency of various occurrences. It can be displayed graphically or as figures in a table or spreadsheet.
Central tendency refers to measurements focusing on a dataset’s usual main values. It does not only refer to the median, which is the average value throughout the entire dataset. Instead, it serves as a generic expression to designate a range of central measurements.
We now know that descriptive statistics’ main goal is to highlight a dataset’s salient characteristics. In contrast, generalisations about a broader population predicated on a representative group of that population are the main goal of inferential statistics. Results from inferential statistics typically take the form of probabilities since they are more focused on generating predictions than expressing facts.
It should come as no surprise that the precision of inferential statistics largely depends on the accuracy and representative sample of the entire population. For this, a random sample must be collected. There is always the assumption that random sampling leads to better outcomes. Conversely, conclusions drawn from skewed or unrepresentative samples are typically disregarded. Although it is not always easy, random sampling is crucial for using inferential techniques.
Inferential Versus Descriptive Statistics: Differences
Let us analyse the differences between descriptive and inferential statistics.
Inferential Versus Descriptive Statistics: Conceptual Difference
The area of statistics known as descriptive statistics is focused on describing the population being studied. On the other hand, a type of statistics known as inferential statistics concentrates on inferring information about the population from sample analysis and observation.
Inferential Versus Descriptive Statistics: Functions
Researchers use descriptive statistics to logically arrange, examine, and present facts. The researchers only describe the dataset’s features using descriptive analysis, which requires using distribution, central tendency, and variability. Descriptive statistics cannot be used to draw inferences from a given dataset. On the other hand, inferential statistics make comparisons between different data points, evaluate the results, test the hypothesis, and make predictions. It usually involves hypothesis testing, confidence intervals, regression, and correlation analysis. Checking the consistency of your samples with your hypothesis is known as hypothesis testing. The goal is to eliminate the possibility that a certain result is the outcome of luck.
Inferential Versus Descriptive Statistics: Final Outcome
The end result of descriptive statistics is displayed using charts, graphs and tables for a comprehensive description of the dataset. On the other hand, the final outcome of results acquired through inferential statistics is probable.
Inferential Versus Descriptive Statistics: Usage
Descriptive statistics are generally used to describe a situation or a data set. In contrast, inferential statistics describes the likelihood that an event will occur based on hypothesis testing and data evaluation. Descriptive statistics merely explains the data and summarises the sample. In contrast, inferential statistics attempt to come to a conclusion about the population that goes beyond what is known from the facts.
Inferential Versus Descriptive Statistics: Key Differences
Descriptive statistics aims to provide a detailed description of the population being studied. A type of statistics known as inferential statistics concentrates on deriving conclusions about the population from sample analysis and observation. Novice researchers are often not familiar with inferential and descriptive statistics, and therefore they experience difficulty in drawing meaningful conclusions. However, if you need help with statistical analysis, you can reach out to experts at dissertation writing services to incorporate descriptive and inferential statistics in your dissertation.
- Collection, organisation, analysis, and meaningful presentation of data are all aspects of descriptive statistics. Inferential statistics, on the other hand, compares data, validates hypotheses, and make predictions about the course of events.
- In contrast to inferential statistics, where the final result is presented as a probability, descriptive statistics show the final result in a graphical representation or tabular format.
- While inferential statistics explains the likelihood of an event occurring, descriptive statistics depict a condition.
- Descriptive statistics explains already known data to describe a sample. The goal of inferential statistics, on the other hand, is to extend or generalise the data and apply it to a broader population.
Now that we have covered both topics in sufficient detail, you just need to remember that while descriptive statistics focuses on presenting your actual dataset, inferential statistics concentrates on establishing assumptions about a population that is separate from the dataset under investigation. Descriptive statistics primary goal is describing and providing a summary of the existing data set, whereas inferential statistics seeks to generalise the dataset beyond the investigated data set.