Decision making today largely depends on the data and information that is collected in huge databases. To organize such information and make it useful for the future, researchers and businesses depend on careful analysis of such data. Statistical analysis allows the examination of the data using certain methods and translates the patterns observed for further development in different fields of science, technology, and business.

  1. What is Statistical Analysis?
  2. Types of Statistical Analysis
  3. Benefits of Statistical Analysis
  4. Statistical Analysis Tools

1. What is Statistical Analysis?

While its definition depends on the context of the industrial application, statistical analysis at its core is the collection and exploration of data to identify quantitative or qualitative patterns and trends. Such patterns can be used to further the development of a business or scientific research. For example, statistical analysis of customers’ purchase activity on an e-commerce website can be used to recommend products to them in the future, thereby, increasing the revenues for the business.

According to Dr. Michael J de Smith, Fellow Royal Statistical Society, “Statistics or Statistical Analysis (plural) in the field of science that involves the collection, analysis, and reporting of information that has been sampled from the world around us”.

Statistical analysis of data coming from a system, a business process, or a scientific study essentially gives a complete picture of its internal working, the patterns in it, and the steps to be taken to enhance, monitor, and evaluate such a system, architecture, or study. Thus, it is crucial to have this tool whenever huge data needs to be given meaning.

Dr. Smith in his Statistical Analysis Handbook describes an iterative way to approach a statistical analysis process called PPDAC which expands to the following:

  • Problem: Understanding and defining the problem to be studied is often a substantial part of the overall analytical process — clarity at the start is a key factor in determining whether a program of analysis is a success or a failure. Ask a lot of questions in this step.
  • Plan: Having agreed on the problem definition the next stage is to formulate an approach that has the best possible chance of addressing the problem and achieving answers (outcomes) that meet expectations.
  • Data: The next step is the collection of data on the problem and following the plan associated with solving it. In research projects that involve experiments, the data are collected within the context of well-defined and (in general) tightly controlled circumstances. In many other instances data is obtained from direct or indirect observation of variates that do not form part of any controlled experiment.
  • Analysis: The Analysis phase can be seen as a multi-part exercise. It commences with the review of data collected and the manipulation of the many inputs to produce consistent and usable data. Descriptive analysis, including the production of simple data summaries, tabulations, and graphs is typically the first stage of any such analysis.
  • Conclusion: The purpose of the conclusion stage is to report the results of the study in the language of the Problem. Concise numerical summaries and presentation graphics should be used to clarify the discussion. As well, the conclusion provides an opportunity to discuss the strengths and weaknesses of the Plan, Data, and Analysis especially in regards to possible errors that may have arisen.

Once the PPDAC process is complete, based on the conclusions, the cycle can be repeated.

2. Types of Statistical Analysis

Though there are different types of analysis, the following two are the main types of statistical analysis or statistical modeling

A) Descriptive statistics

As the name suggests, descriptive statistics explicates the information during data analysis. Descriptive analytics is concerned with the quantitative description of data, its organization, and a potential overview of the information it holds. Two types of statistics are used to describe data:

  • Measures of central tendency: In this, a single value attempts to describe the data by using its central position with the given set. Mean, median, and mode of the data are measures of central tendency.
  • The measure of spread: In this, the data is summarized by describing how well the data is spread out. Range, quartiles, standard deviation, variance, and absolute deviation are used to measure the spread.

Graphs and charts are generally used during descriptive analysis to understand data.

B) Inferential Statistics

Using the data from the samples and making generalizations about a population is done using inferential statistics. Getting to quantitative or qualitative reasoning (inference) based on data is exercised in this type of statistics. There are two main areas of inferential statistics:

  • Estimating Parameters:  This means taking a statistic from your sample data (for example the sample mean) and using it to say something about a population parameter (i.e. the population mean)
  • Hypothesis Tests: Here the sample data can be used to answer research questions. It is a way for you to test the results of a survey or experiment to see if you have meaningful results. We can interpret data by assuming a specific structure of our outcome and use statistical methods to confirm or reject the assumption.

3. Benefits of Statistical Analysis

Careful analysis of the data produced during a scientific experiment, market research, business process analysis can become crucial to quantitatively understand the abstract nature of such activities and their impact.

The most common benefit of statistics is the quantification of business’ performance. Analyzing the year over year turnouts in revenue, customer satisfaction, employee satisfaction, and overall financial progress can give a solid picture of a company’s performance. Moreover, attrition analysis can give crucial information about the internal structural problems in the human resource employed by a company.

An intelligent understanding of data can be used to predict phenomena like weather, stock market, etc. to name a few. For example, Apple uses predictive statistics to disallow the overcharging of current iPhones to save batteries from damage.

4. Statistical Analysis Tools

An individual can’t perform analysis of huge amounts of data by hand. There are many statistical tools provided by different creators to do such tasks with ease. The tools can be categorized into two classes:

A) With a Graphical User Interface

Tools in this category largely allow descriptive and inferential statistics using the user interface elements.

  • MS Excel
  • SAS
  • Tableau

B) Programming Based

These tools will help one who wants full control of their analysis and knows the programming language.

  • Revolution Analytics’ R
  • Python


Statistical data analysis strives to find patterns inside huge amounts of data. Description of such data with quantitative laconicism benefits decision making in scientific experiments and business growth. While descriptive statistics deal with the quantitative description of data, inferential statistics generalized the observations from samples to derive inferences for the population. Benefits of statistical analysis of data when following statistical analysis methods like PPDAC can eradicate the need for gut-feelings. Statistical analytical tools like MS Excel and R make it easy to do huge analyses in a short amount of time.

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