Descriptive and Inferential Statistics

 

a.      Descriptive Statistics:

A descriptive statistic  is a summary statistic that quantitatively describes or summarizes features of a collection of information. Descriptive statistics describe what is going on in a population or data set. 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.

b.     Inferential Statistics:

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.

Techniques that social scientists use to examine the relationships between variables, and thereby to create inferential statistics, include linear regression analyses, logistic regression analyses, ANOVA, correlation analyses, structural equation modeling, and survival analysis. When conducting research using inferential statistics, scientists conduct a test of significance to determine whether they can generalize their results to a larger population. Common tests of significance include the chi-square and t-test. These tell scientists the probability that the results of their analysis of the sample are representative of the population as a whole.

Descriptive Statistics	Inferential Statistics
a.It is that branch of statistics which is concerned with describing population under study.	i.It is a type of statistics which focuses on drawing conclusion about the population on the basis of sampling.
b.It organizes, analyses and presents the data in a meaningful way.	ii. It compares, tests and predicts data.
c.It presents the data in graphs and tables.	c.It presents the data in probability.
d.It is used to describe a situation.	iv.It is used to explain the chart of occurrence of an event.
e.It explains the data which is already known to summarize sample.	v.It attempts to reach the conclusion to learn about the population that extends the available data.
f.It uses methods like mean, mode, median, standard deviation, variance.	vi.It uses methods like regression analysis, testing of hypothesis, estimation of parameters.

Some parts are adopted from: https://www.thoughtco.com/differences-in-descriptive-and-inferential-statistics-3126224