Basics of Statistical Analysis: Types, Terms, Steps, Objectives and Merits

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Definition and Introduction:

Statistics is referred to as a methodology developed by scientists and mathematicians for collecting, organizing and analyzing data and drawing conclusions from there. More precisely, the statistical analysis gives significance to insignificant data or numbers.

Statistics is “a branch of mathematics dealing with the collection, analysis, interpretation, and presentation of masses of numerical data."- Merriam-Webster dictionary

Statistics is referred to as “Numerical statements of facts in any department of inquiry placed in relation to each other." –Statistician Sir Arthur Lyon Bowley

Statistics began its journey in the 5th Century BC but it started drawing attention more than Calculus and Probability theory in the 18th Century BC.

Types of Statistical Method:

Statistical methods are of two types:

  • Descriptive Method: This method uses graphs and numerical summaries.
  • Inferential Method: This method uses confidence interval and significance test which are part of applied statistics.

Basic Terms of Statistics:



Population refers to the large group of individuals or objects from where a researcher or investigator starts his/her research or investigation.


Sample refers to a randomly selected set of individuals or objects from the population.

Parameters and Statistics:

  • An unknown numerical briefing of the population is a parameter.
  • A known numerical briefing of the sample, used in making inference about the parameter is called statistics.
  • A statistic illustrates a sample whereas a parameter illustrates the population from where the sample was selected.

Basics of Descriptive Statistics:

  • Mean:

The mean also referred to as average is calculated by dividing the total value or sum of the examined values by the number of the observations.

For example: 20, 30,60, 80, 90

Mean/Average= (20+30+60+80+90)/5=280/5=56

  • Standard Deviation:

The standard deviation provides a concept of the closeness of the whole set of data to the mean or the average value. A small standard deviation denotes that the data sets are tightly grouped. A large standard deviation denotes that the entire data sets are scattered to a wide range of values.

  • Variance:

The variance calculates the extent to which a set of data is spread out. A zero variance denotes that all the values are similar. Variance is the squared value of the standard deviation, so it can never be negative. A small variance denotes that the data points are very close to the average value or the mean. On the other hand, a large variance denotes that the data points are very widely spread around the average value or the mean.

Steps of Statistical Data Analysis:

The aim of statistics is to draw a conclusion from data. Any data analysis involves the following steps:

  1. Starting
  2. Formulating the research problem
  3. Defining population and sample
  4. Performing descriptive data analysis
  5. Using appropriate statistical methods to solve the research problem
  6. Reporting the outcome or result.

Objectives of Statistical Analysis:

Statistical analysis has the following objectives:

  • Defining the type and quantity of data need to be collected.
  • Organizing and summarizing the data.
  • Analyzing the data and drawing conclusions from it.
  • Assessing the strengths of the conclusions and evaluating their uncertainty.

Merits of Statistical Analysis:

  • Provides a design: Statistical analysis provides a design for planning and completing the research studies.
  • Provides a description: Statistical analysis provides a description of data by organizing and summarizing the data.
  • Provides a conclusion: Statistical analysis provides a conclusion by making anticipations and generalizing the phenomena illustrated by the data.


Business people should have the basic knowledge of statistical analysis for collecting, organizing and analyzing data and drawing conclusions from there to make effective business decisions.

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