How Statistical Analysis Methods Take Data to a New Level in 2023

If your business isn’t taking a close look at its data, there’s a whole world of possibilities that you're missing.

With the help of business analytics, your organization can do more with its data than ever before, you just have to know where to start. Whether making predictions or finding trends, using statistical analysis software is your best bet for the insights you need.

There is a lot that a business can do with its big data, and statistical analysis is a way for organizations to examine and learn from it in a smart way. It deals with a variety of components of data, including data collection, surveys, and experiments.

As an aspect of business intelligence, statistical analysis scrutinizes business data and reports on trends using five key steps.

• Describe the type of data that will be analyzed
• Explore the relation of the data to the underlying population
• Create a statistical model to summarize the understanding of how the data related to the underlying population
• Prove or disprove the validity of the model
• Use predictive analytics to run scenarios that will guide future actions

In statistics, a population is the entire group of data being analyzed. This may refer to data like a whole group of people, objects, animals, how many visits are taken to the hospital in a year, events, or even measurements. It can be any size if it includes all the analyzed data.

Importance of statistical analysis 

Once the data is collected, statistical analysis can be used for many things in your business. Some include:

• Summarizing and presenting the data in a graph or chart to present key findings
• Discovering crucial measures within the data
• Calculating if the data is slightly clustered or spread out, which also determines similarities
• Making future predictions based on past behavior
• Testing a hypothesis from an experiment

There are several ways that businesses can use statistical analysis to their advantage. Some of these ways include identifying who on your sales staff is performing poorly, finding trends in customer data, narrowing down the top operating product lines, conducting financial audits, and better understanding how sales performance can vary in different regions of the country. 

Statistical analysis vs. data analysis

In business intelligence, it’s common to confuse statistical analysis and data analysis to solve various problems an organization may experience. However, the data analytics tools and overall process have key differences compared to the common types of statistical analysis.

Statistical analysis applies specific statistical methods to a sample of data to understand the total population. It allows for conclusions to be drawn about particular markets, cohorts, and a general grouping to predict the behavior and characteristics of others.

Data analysis is the process of inspecting and cleaning all available data and transforming it into useful information that can be understood by non-technical individuals. This is crucial when you consider that data can be meaningless if it isn’t understood by those who make decisions. 

Data analysis can be used as an input to perform statistical analysis, as data from varying sources can be combined to carry out the statistical process.

Main types of statistical analysis

When applying statistical analysis to your business, the two main types you’ll use are descriptive and inferential. However, there are other types that many businesses also use, depending on the overall goal or question the organization is looking to answer.

Descriptive statistical analysis

Descriptive analysis creates simple reports and graphs using data visualization software that lets companies understand what happened at a particular point. It’s important to note that descriptive analysis only pertains to events that occurred in the past.

The actual data part of descriptive analytics focuses on answering “what happened” in a way that takes a deep dive into past data.

As the name suggests, it’s used to describe the basic features of past information and summarize it easily and rationally. It’s important to keep in mind that this type of statistical analysis isn’t used to draw conclusions. You can only describe what something is and what the data from the past represents.

For example, a business may use descriptive statistical analysis to look at your company’s traffic throughout the past year. You can see things like when traffic fell, when it picked back up, which month had the most traffic, and the average traffic of each month. However, this data doesn’t tell you why traffic fell.

Descriptive statistical analysis only describes the data or summarizes the information surrounding the data. It’s still important to understand your data in a meaningful way.

The goals of descriptive analysis are:

• Describe data in a visual manner
• Explain what the data presents

Inferential statistical analysis

While descriptive statistics has a limitation in that it only allows for broader assumptions about the data, objects, or people you measure, inferential statistical analysis can solve that limitation.

Inferential statistics is the result of more complicated and mathematical estimations. The analyzed samples allow users to infer or conclude trends about a larger population. It takes data from a sample and then makes conclusions about a larger population or group.

Inferential statistical analysis is often used to study the relationship between variables within a sample, allowing for conclusions and generalizations that accurately represent the population. And, unlike descriptive analysis, businesses can test a hypothesis and come up with various conclusions from this data.

As an example, you want to know the favorite pizza topping of everyone in the world. Chances are you’re not going to go out and interview everyone in the world. Instead, you’d try to sample a representative population of people and attempt to hypothesize your results.

From a more business and realistic standpoint, you may want to ask every single one of your customers a question about your product or service. If you have 100,000 customers, polling every one of them may be challenging. Instead, you’d go with a sample group of customers.

While this process isn’t perfect, and you may find it difficult to avoid errors, it makes it simple for researchers to make reasoned inferences about the population.

The goals of inferential statistics are:

• Draw conclusions from analyzed data
• Test various hypotheses surrounding the data

Other types of statistical analysis

Beyond those two main types, there are other examples of statistical analysis that businesses should know.

Predictive analysis

When making predictions about future events your business could experience, turn to predictive analytics, which details what is likely to happen next. This analysis is based on current and historical facts and uses statistical algorithms and machine learning to define the likelihood of future trends based on historical data.

The industries that get the most out of predictive analytics are marketing companies, insurance firms, and financial services, but any business can see a massive benefit from preparing for an unpredictable future.

The purpose of predictive analytics is:

• Anticipate future events using data
• Determine the likelihood of various trends in behavior

Prescriptive analysis

​​Prescriptive analysis can be extremely complex, so it has yet to be widely used by businesses when performing statistical analysis.

While other analytics tools can be used to draw conclusions, prescriptive analysis provides you with actual answers. A high level of machine learning usage is needed for these types of reports since they provide actions to take next. It also uses techniques such as complex event processing, graph analysis, and simulation.

When using prescriptive analytics, the objectives are:

• Answer the question, “What should be done next?”
• Narrow down the correct recommendation for a specific decision

Exploratory data analysis

Exploratory data analysis, also known as EDA, focuses on identifying patterns in the data to find potential unknown relationships.

The purpose of this method is to:

• Discover new connections within the data
• Check for missing data or mistakes within the data collection
• Collect as much insight as possible surrounding the data set
• Review assumptions and hypotheses

Casual analysis

If your business objective is to understand and identify why things happen, casual analysis is the route you should take.

No matter your organization's industry, you’re bound to experience failure. Causal analysis is used to determine why failures happen and narrow down the root of the cause.

One example of causal analysis is in the IT field as businesses perform quality assurance on various software. This type of statistical analysis would be used to examine why specific software failed, if there was a bug, a data breach, or a DDoS attack.

The goals of causal analysis are:

• Identify key problem areas within the data
• Investigate and determine the root cause of why a failure occurred

Mechanistic analysis

Out of all of the types of statistical analysis, mechanistic analysis is the least common. However, in the sense of big data analysis and biological science, it plays a crucial role in the process.

This method consists of understanding specific changes in variables that cause other changes in other variables. It doesn’t consider external influences outside of a business’s control, such as temperature and time. This method is used to show how things happen rather than telling how things will occur later on, so it isn’t used to make predictions.

An example of mechanistic analysis in action is when those in biological science study viruses and inspect how various parts of the virus are affected by changes in medicine.

The purpose of mechanistic analysis is:

• Understand the exact changes in variables that will lead to changes in other variables
• Make known that any data was a result of what happened when a subject was going through a specific activity

Best statistical analysis software

When it comes to statistical analysis software, there’s a wide variety of options you can choose from. This software utilizes specialized programs designed to allow its users to perform complex statistical analysis that they’re unable to do by hand. 

Statistical analysis software tools are typically used by data scientists and mathematicians but can provide industry-specific features. Each tool provides a unique set of features that your company may find to be exactly what it needs. 

To qualify for inclusion in the statistical analysis category, a product must:

• Offer statistical analysis capabilities, equations, and models
• Provide data importing, preparation, and modeling
• Perform complex statistical analysis

* Below are the top 5 leading statistical analytics software solutions from G2’s Summer 2023 Grid® Report. Some reviews may be edited for clarity. 

1. IBM SPSS Statistics 

IBM SPSS Statistics helps businesses of all sizes solve industry-specific issues that drive decision-making. It offers advanced statistical procedures and visualization elements, providing users with a robust and user-friendly platform to help understand data and complex problems. 

What users like best:

“There are three elements that I would like to highlight about SPSS. First, its easy-to-use interface has a practical design that is very similar to other more well-known programs. Second, the wide variety of functions and statistical calculations that can be performed, from descriptive statistics to multivariate techniques, and finally, the customization of statistical analyses that allows for a wide range of processes.”

- IBM SPSS Statistics Review, Alejandro G.

What users dislike:

“Integrations could be improved. Also, programming tasks could be more efficient. That way it could be possible to automate certain processes.”

- IBM SPSS Statistics Review, Guillermo R.

2. JMP

JMP is a data analysis tool for Mac and Windows that combines the power of interactive visualization with insightful statistics. It makes it easy for users to import and process data, utilize the drag-and-drop interface, dynamically linked graphs, and so much more. Through cutting-edge statistical methods and speed analysis techniques, JMP provides a long list of functionality and features.

What users like best:

“I like the ability to run simple statistical tests. It can be used to create graphs from data. It is incredibly easy to use. It provides extensive examples for each test. It is very useful for plotting data. I love how easy it is to use.”

- JMP Review, Rogerio M.

What users dislike:

“While the interface is great, I wish there were more immediate and direct ‘help’ options within the program. Maybe even something like tutorials you could run (within the program, not having to download or look on the internet) when you ask to conduct a test for the first time. Maybe use some sort of real-language system to enable the program to tell you a suggested test to run based on certain input.

At times, some of the output is also a little confusing, especially for students that are just learning - getting large tables and graphs as output, when all you want is a p-value, can be daunting.”

- JMP Review, Christopher V.

3. Minitab Statistical Software

Minitab Statistical Software offers visualizations, statistical analysis, predictive analytics, and improvements analytics to help businesses make smart decisions based on data. From spotting trends to solving problems, Minitab delivers comprehensive data analysis to all types of organizations. 

What users like best:

“I've been using Minitab for years, and I've always been impressed with how powerful it is. It offers an extensive range of statistical tools. It can be used to analyze data from different types of variables, such as categorical variables and continuous variables.”

- Minitab Statistical Software Review, Mary B.

What users dislike:

“Minitab can improve its features by enabling additional data handling as well as analytical tools which will help us to solve complex data correlation easily.”

- Minitab Statistical Software Review, Anshuman S.

4. Posit

Posit creates open-source software for scientific research, technical communication, and data science. This tool helps individuals, teams, and enterprises with data science, actionable decision making, and more.

What users like best:

“Posit is so user-friendly and easily accessible. Their product RStudio is also excellent. We can do anything with it, like data pre-processing, analysis, model building, and visualization.”

- Posit Review, Samrit P.

What users disike:

“A lack of programmatic functionality can make large dynamic loop functions painful to implement. Debugging tools are also somewhat limited compared to mainstream IDEs.”

- Posit Review, Brodie G.

5. Base SAS

Base SAS provides users with programs for data manipulation, descriptive reporting, information storage and retrieval, and a centralized metadata repository. Users of Base SAS can significantly reduce programming and maintenance time, be able to integrate data across environments, and simplify the creation and distribution of visually appealing reports.

What users like best:

“Base SAS is the best analytical tool, especially in the clinical trial domain and research, which needs statistical procedures. This provides easy-to-use-in-build methods with many options, fewer codes, and less complexity.”

- Base SAS Review, Ruchi S.

What users dislike:

“The capacity of the system is essentially subordinated to the standard paradigms. The technical support they provide is not very astute and is often scarce (conditionally it is not free, there are no different audiences in which we can raise possible problems and solutions). In addition, it is not very perceptive for the consumer and its use is complex at the beginning.”

- Base SAS Review, Madhur K.

A method to the madness

No matter which method of statistical analysis you choose, make sure to take special note of each potential downside, as well as their unique formula.

Of course, there’s no gold standard or right or wrong method to use. It’s going to depend on the type of data you’ve collected, as well as the insights you’re looking to have as an end result.

When it comes to correlation and regression, do you know which one you should be using? Find out here.