7 Types of Statistical Analysis: Definition and Explanation | analysis steps (2023)

Statistics is the branch of science that provides various analytical tools and techniques to process large amounts of data. Put simply, it is the science of collecting, classifying, analyzing, and interpreting and expressing the numerical form of data to make inferences about populations. , from selected sample data that can be used by business professionals to solve their problems.

Therefore, to organize data and predict future trends depending on the information, many organizations rely heavily on statistical analysis.

But exactlystatistical data analysisrefers to the collection, interpretation and presentation of data. It can be addressed when dealing with data to solve complex problems. More specifically, statistical analysis gives meaning to insignificant/irrelevant data or numbers.

The main types of statistical analysis are

Specifically, statistical analysis is the process of consolidating and analyzing different samples of data to uncover patterns or trends and anticipate future events/situations in order to make appropriate decisions.

Statistical analysis has the following types, which depend heavily on the data types.

7 types of statistical analysis

1.Descriptive statistical analysis

Basically, it's all about organizing and summarizing data using numbers and graphs. It provides huge amounts of data for understandable interpretation, even without drawing conclusions or answering hypotheses beyond analysis.

Rather than processing data in its raw form, descriptive statistical analysis allows us to present and interpret data more efficiently through numerical calculations, graphs or tables.

From all the necessary preparatory steps to the completion of the analysis and interpretation,descriptive statistical analysisincludes several processes such as tabulation, a measure of central tendency (mean, median, mode), a measure of spread or variance (range, range, standard deviation), measures of skewness, andtime series analysis.

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In the descriptive analysis, the data are summarized in tabular form and managed and presented in the form of tables and graphs for the sum of the data assuming the total population.

Also read |Descriptive Statistics in R

In addition, it helps to extract different characteristics from the data and to summarize and explain the essential characteristics of the data. In addition, no information is obtained about the unobserved/sampled groups.

2.Inferential Statistical Analysis

Inferential statistical analysis is generally used when examining each unit of the population is not feasible, so the information obtained is extrapolated to the entire population.

In simple terms, inferential statistical analysis allows us to test a hypothesis based on a sample of data from which we can draw inferences, applying probabilities and making generalizations about any data, and we can also draw conclusions about future outcomes move beyond the available data. .

Therefore, it is very preferable to draw conclusions and make decisions about the entire population based on sample data. Therefore, this method involves sampling theory, various significance tests, statistical control, etc.

Descriptive statistical analysis vs. inferential

3.predictive analytics

Predictive analytics is implemented to make a prediction about future events or what is likely to happen next based on current and past facts and figures.

in simple words,Predictive analytics uses statistical techniques andMachine Learning Algorithmsto describe the possibility of future outcomes, behaviors and trends based on current and historical data. Techniques widely used in predictive analytics include data mining, data modeling, artificial intelligence, machine learning, etc. to make compelling predictions.

In today's business system, this analysis is used by marketing companies, insurance companies, online service providers,Datengetriebenes Marketingand financial companies, however, any company can benefit by planning for the unpredictable future, how to gain a competitive advantage and reduce the risk associated with an unpredictable future event.

Predictive analytics converge in predicting future events using data and checking the likelihood of various trends in the behavior of the data. Therefore, companies use this approach to get the answer "What can happen?". where the basis for predictions is a probability measure.

4.Prescriptive Analytics

Prescriptive analytics analyzes data to determine what to do. It is commonly used in business analysis to determine the best possible course of action for a situation.

While other statistical analyzes can be implemented to generate exclusions, they provide the actual answer. Basically, it focuses on finding the optimal signal for a decision-making process.

Various techniques implemented within the framework of prescriptive analytics are simulation, graph analysis, algorithms, complex event processing, machine learning,recommendation engine, business rules, etc.

However, it is almost related to descriptive and predictive analytics, where descriptive analytics explains data in terms of what happened, predictive analytics anticipates what might happen, and here prescriptive analytics is about making appropriate suggestions between available preferences.

5.Exploratory Data Analysis (EDA)

Exploratory data analysis, or also called EDA, is a counterpart of inferential statistics and is widely used by data scientists. It is usually the first step in the data analysis process, performed before any other statistical analysis technique.

EDA is not itself implemented to predict or generalize, it provides a visualization of the data and helps to get important information about it.

This method focuses entirely on analyzing patterns in the data to identify possible connections. EDA can be contacted to discover unknown associations in data, examine missing data from collected data and gain maximum insights by examining assumptions and hypotheses.

6.root cause analysis

In general, causal analysis helps to understand the reasons why and determine "why" things happen or why things are the way they seem.

For example, in the current business environment, there are many ideas or companies that have failed due to occurrence of some events; In this state, causal analysis identifies the root cause of the failures, or just the main reason why something might be happening.

noIT Industrie, this is used to check the quality control of a specific software, such as why that software failed, whether there was a bug, a data breach, etc., and prevents companies from suffering major setbacks.

We can consider a causal analysis if;

• Identify significant problem areas within the data,
• Investigate and identify the root causes of the problem or failure,
• Understand what happens to a given variable when another variable changes.

7.mechanistic analysis

Among the above statistical analyses, the mechanistic is the least common type, but it pays off in the processbig data analysisand life sciences. It is implemented to understand and explain how things happen and not how certain things will happen later.

It uses the clear concept of understanding individual changes in variables causing changes in other variables, excluding external influences and considering the assumption that the whole system is influenced by the interaction of its own internal elements.

The basic goals of mechanistic analysis include:

• Understand definitive changes where you might make changes to other variables
• A clear explanation of the occurrence of a past event related to the data, particularly if the specific issue/concern relates to specific activities.

For example in life sciences to study and study how different parts of the virus are affected by changes in medicine.

In addition to the types of statistical analysis mentioned above, it is worth noting here that these statistical treatments, orstatistical data analysis techniques, are highly dependent on how the data is used. Although it depends on the role and needs of a particular study, data and statistical analysis can be used for many purposes, for example, medical professionals can use a variety of statistical analyzes to test a drug's efficacy or potency.

Also, a large amount of available data can reveal a lot that data practitioners want to investigate, so statistical analysis can provide some informative results and some conclusions. In addition, in some cases, statistical analysis may be performed to gather information about people's preferences and habits.

For example user data, on sites like Facebook andInstagram, can be used by analysts to understand user perception, e.g. B. what users do and what motivates them. This information may benefit commercial ads in which you target a specific group of users in order to sell them things. It is also useful for application developers to understand user reactions and habits and make changes to products accordingly.

Conclusion

A deeper understanding of data can expand the myriad possibilities for an organization through the implementation ofBusiness Analyst, an organization can achieve through the study of data, for example, to make predictions, insights or conclusions from data, and this can be done, for example, by statistical analysis;

• Collect and manifest data in the form of charts or tables to show key results,
• Examine meaningful data items/metrics such as mean, variance, skewness, etc.
• Test a hypothesis from multiple experiments,
• Anticipate future predictions based on past data behavior and more.

Therefore, a company can take advantage of statistical analysis in many ways, e.g. B. to identify underperforming sales, identify trends in customer data, conduct financial audits, etc.

Also read |Bayesian statistics

Rather, we saw two main types.”descriptive and inferential statistical analysis” is to be chosen when applying statistical analysis to a business problem; However, other types of statistical analysis are becoming a priority when it comes to meeting other business needs that organizations are looking for, based on intent or full queries.