Statistical Analysis: Types, Definition and Explanation

Statistics is the science of assembling, classifying, analysing, and manifesting the numeric form of data for drawing conclusions about the population from the selected sample data that can be used by business experts to solve their problems. Statistics is a branch of science that offers a variety of tools and analytical techniques to deal with the enormous extent of data.

Depending on the information, many organisations largely rely on statistical analysis to organise data and forecast future trends.

Statistical data analysis deals with the gathering, interpreting, and presentation of data. It can be used to tackle complex challenges when working with data. More specifically, statistical analysis gives meaning to facts or statistics that would otherwise be meaningless or useless.

Types of Statistical Analysis

Descriptive Statistical Analysis

Descriptive statistical analysis involves with categorising and condensing data into numerical and graphical representations. It simplifies the vast amounts of data for understandable interpretation even without drawing any additional conclusions from the analysis or addressing any hypotheses.

It allows us to more effectively depict and analyse data using numerical calculation, graphs, or tables as opposed to processing data in its raw form.

Descriptive statistical analysis involves a number of processes, including tabulation, a measure of central tendency (mean, median, mode), a measure of dispersion or variance (range, variation, standard deviation), skewness measurements, and time-series analysis, from all necessary preliminary steps to the concluding analysis and interpretation.

When conducting a descriptive analysis, the data is tabulated, organised, and displayed in the form of charts and graphs to sum it up and represent it for the entire population.

Additionally, it aids in identifying distinct properties of data as well as in summarising and illuminating its fundamental characteristics. Furthermore, no conclusions are made about the groups that are not observed or sampled.

Inferential Statistical Analysis

When it is not possible to inspect every unit in the population, inferential statistical analysis is typically performed to extrapolate the information to the entire population.

In layman’s terms, inferential statistical analysis allows us to test a hypothesis based on a sample of data from which we can draw generalisations about the entire data and test hypotheses about future outcomes that go beyond the data currently available.

In this sense, using sample data to infer generalisations and make decisions regarding the entire population is strongly advised. This method, therefore, incorporates the sampling principle, numerous significance tests, statistical control, etc.

Predictive Analysis

Utilising data from the present and the past, predictive analysis is used to create predictions about what is likely to happen next.

A business can benefit from this analysis by planning for an unpredictable future to gain a competitive edge and reduce the risk associated with an unpredictable future event. In the current business system, this analysis is approached by marketing firms, insurance companies, online service providers, data-driven marketing, and financial corporations.

The focus of predictive analysis is on using data to anticipate future events and determine the likelihood of certain trends in data behaviour. Businesses, therefore, employ this strategy to answer the question “What might happen?” where generating predictions is based on a probability estimate.

Prescriptive Analysis

Prescriptive analysis, which looks at the facts to determine what should be done, is frequently used in business analysis to determine the best course of action in a given circumstance.

For driving exclusions, different statistical analyses may be used, but this one offers the correct response. It basically focuses on finding the best proposal for a decision-making process.

Simulation, graph analysis, algorithms, complicated event processing, machine learning, recommendation engines, business rules, etc. are some of the tools used in prescriptive analysis.

However, it is closely related to descriptive and predictive analysis. Whereas the former clarifies data in terms of what has already occurred, the latter predict what might occur. In this case, prescriptive analysis works by making sensible recommendations among the available preferences.

EDA – Exploratory Data Analysis

Inferential statistics’ counterpart, exploratory data analysis (EDA), is frequently used by data specialists. Before using any other statistical analysis methods, it is typically the first step in the data analysis process.

EDA is used to preview data and help with crucial insights rather than being used solely for generalisation or prediction.

This approach completely focuses on looking for patterns in the data to identify possible correlations. EDA can be used to examine assumptions and hypotheses, evaluate missing data from collected data and gain the most insights, and identify undiscovered relationships within data.

Casual Analysis

Causal analysis helps in comprehending and figuring out the reasons “why” things happen or why things are the way they seem to be.

This is used in the IT sector to examine the software’s quality assurance, including the reasons why it malfunctioned, whether a bug caused a data breach, etc., and protects businesses from serious setbacks.

Mechanistic Analysis

Mechanistic statistical analysis is the least prevalent of the aforementioned types, yet it is important for biological science and large data analytics. It is used to comprehend and explain what occurs rather than to predict what will happen in the future.

It makes use of the simple idea that understanding individual changes in one variable leads to changes in other variables in a corresponding manner while ignoring external influences and taking into account the presumption that the entire system is affected by the interaction of its own internal constituents.

In addition to the aforementioned sorts of statistical analysis, it is essential to note that these statistical treatments, or statistical data analysis procedures, heavily depend on how the data is used. Data and statistical analysis can be used for a range of purposes depending on the function and requirements of a given study. For instance, medical professionals can use a variety of statistical analyses to examine the efficacy or potency of a drug.

In addition, since there is a wealth of information available that can shed light on a variety of topics, data scientists are interested in investigating, and statistical analysis can produce results that are instructive and allow for judgements. Additionally, in some circumstances, statistical analysis can be used to gather data on people’s preferences and routines.

With the application of business analytics, an organisation can achieve this while scrutinising data, for example, driving predictions, insights, or conclusions from data, and this is what statistical analysis can do. A deeper understanding of data can broaden the many opportunities for a business.

As a result, a firm can benefit from statistical analysis in a variety of ways, such as assessing sales performance, identifying trends in customer data, conducting financial audits, etc.