Difference Between Descriptive and Inferential Statistics

In today's fast-paced world, statistics plays a significant role in research, Which helps collect, analyze and present data in a measurable form. However, it is difficult to identify whether research relies on descriptive or inferential data, as people generally lack knowledge about these two branches of statistics. As the name suggests, descriptive statistics is that which describes the population. 

On the other hand, inferential statistics are used to make generalizations about a population based on samples. As a result, there is a significant distinction between descriptive and inferential statistics, i.e. what you do with your data. Let's look at this article to get more details on the two topics.

Descriptive statistics

Descriptive statistics are often related to the term "statistics". The goal of this branch of statistics is to describe numerical figures. Numerical measures are used to describe the characteristics of a set of data. This part of the statistics contains several items, such as:

• The average or measure of the centre of a data set consisting of the mean, median, mode, or mid-range
• The spread of a data set, which can be measured by the range or standard deviation
• Overall description of data such as five number summary
• Measurements such as skewness and kurtosis
• Exploring relationships and correlations between paired data
• Presentation of statistical results in graphical form

These measures are essential and valuable because they allow scientists to see patterns between data and thus allow understanding of that data. However, descriptive statistics can only describe the population or data set under study: the results cannot be generalized to any other group or population.

Types of Descriptive Statistics

There are two types of descriptive statistics that social scientists use:

Measures of central tendency capture general tendencies within data and are calculated and expressed as mean, mean, and mode. The mean tells scientists the mathematical average of all data sets, such as the mean age at first marriage; The median represents the middle of data distribution, such as the age that sits in the middle of the age range at which people first marry; And, this method may be the most common age at which people get married for the first time.

Diffusion measures describe how data is distributed and related to each other, including:

• Range, the full range of values ​​in the data set
• Frequency distribution, which defines how often a particular value occurs within a data set
• Quartiles are formed within a data set, subgroups when all values ​​are divided into four equal parts
• Mean Absolute Deviation, the average of how much each value differs from the mean
• Variance refers to how much dispersion is present in the data
• standard deviation, which shows the spread of the data relative to the mean

Measures of spread are often represented visually in tables, pie and bar charts, and histograms to help understand trends within the data.

Inferential Statistics

Inferential Statistics are produced through complex mathematical calculations that allow scientists to predict trends about a large population based on the study of a sample taken from it. For example, scientists use inferred statistics to examine 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 inspect each member of the population individually. So scientists choose a representative subset of the population, called a statistical sample, and from this analysis, they can say something about the population from which the sample came. There are two significant sections of inferential statistics:

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

Social scientists use techniques to examine relationships between variables and thus create predictive statistics, including linear regression, logistic regression analysis, ANOVA, correlation analysis, structural equation modelling, and survival analysis. When conducting research using inferred data, scientists test significance to determine whether they can generalize their results to a larger population. Standard tests of significance include chi-square and t-test. These tell scientists the probability that the results of their sample analysis represent the population as a whole.

What is the difference between inference and descriptive statistics?

• Inferential statistics use probabilities and can be used to determine the probabilities of an outcome in future experiments.
• Descriptive statistics describe the information available and are used to find the data's centre, size, and variability.
• Inferential statistics is more important than descriptive statistics as it helps make predictions about a larger population based on the data collected from a subset of that population.
• However, both play an essential role in any data analysis.
• Inferential vs descriptive comparison is a standard comparison in data analysis.
• Whereas descriptive statistics only describe the data, inferential statistics use probabilities to find answers about a population.
• Inferential statistics are more important than descriptive statistics as they help predict large populations.

Conclusion

Inferential vs descriptive is a standard comparison in data analysis. Whereas descriptive statistics only describe the data, inferential statistics use probabilities to find answers about a population. Inferential statistics are more important than descriptive ones as they help make predictions about larger populations. However, both are essential for data analysis in general.

Statistics is a branch of mathematics that deals with interpreting, collecting, analyzing, presenting and organizing data. It is used in various fields, including business, economics, health care and sociology. Most of the students find statistics to be a challenging subject. This is because it involves a lot of ideas that are new to them. Plus, there are a lot of formulas and techniques to apply. If you are struggling with your statistics course, don't worry. You can get our professionals' help through guided sessions. Value Assignment Help is always there for you! Just ask us by filling out the form.