In an effort to organize their data and predict future trends based on the information, many businesses rely on statistical analysis.
While organizations have lots of options on what to do with their big data, statistical analysis is a way for it to be examined as a whole, as well as broken down into individual samples.
The online technology firm TechTarget.com describes statistical analysis as an aspect of business intelligence that involves the collection and scrutiny of business data and the reporting of trends.
"Statistical analysis examines every single data sample in a population (the set of items from which samples can be drawn), rather than a cross sectional representation of samples as less sophisticated methods do," TechTarget writes on its website.
They point to specific ways in which statistical analysis is completed. They said five steps are taken during the process, including:
- Describe the nature of the data to be analyzed.
- Explore the relation of the data to the underlying population.
- Create a model to summarize understanding of how the data relates to the underlying population.
- Prove (or disprove) the validity of the model.
- Employ predictive analytics to anticipate future trends.
Business analytics software and services provider SAS defines statistical analysis as the science of collecting, exploring and presenting large amounts of data to discover underlying patterns and trends.
Dan Sullivan, an author, systems architect, and consultant with over 20 years of IT experience with engagements in systems architecture, enterprise security, advanced analytics and business intelligence, says there are several ways in which businesses can use statistical analysis to their advantage, including finding the top performing product lines, identifying poorly performing sales staff and getting a sense of how varied sales performance is between regions of the country.
In a blog posting on Tom's IT Pro, Sullivan writes that statistical analytic tools can be used to help with predictive modeling. Rather than show simple trend predictions that can be affected by a number of outside factors, he said statistical analysis tools allow businesses the ability to dig deeper to see additional information.
"Statistical tools can help you discover those additional pieces of information," Sullivan wrote.
Types of statistical analysis
There are two main types of statistical analysis: descriptive and inference, also known as modeling.
According to the website My Market Research Methods, descriptive statistics is what organizations use to summarize their data.
"Descriptive statistics intend to describe a big hunk of data with summary charts and tables, but do not attempt to draw conclusions about the population from which the sample was taken," the company writes on its website. "You are simply summarizing the data you have with pretty charts and graphs — kind of like telling someone the key points of a book (executive summary) as opposed to just handing them a thick book (raw data)."
Since charts, graphs and tables are primary components, descriptive statistics makes it easier to understand and visualize raw data. Laerd Statistics, which helps students with their statistic work, notes that descriptive statistics are simply a way to describe data and are not used to make conclusions beyond the analyzed data or reach conclusions regarding any hypotheses that were made.
"Descriptive statistics therefore enables us to present the data in a more meaningful way, which allows simpler interpretation of the data," Laerd writes on its website.
Among some of the useful data that comes from descriptive statistics includes the mode, median and mean, as well as range, variance and standard deviation.
The second type of statistical analysis is inference. Inferential statistics are a way to study the data even further.
According to My Market Research, inference statistics allows organizations to test a hypothesis and draw conclusions about the data. In these cases, a sample of the entire data is typically examined, with the results applied to the group as a whole.
According to online textbook provider Boundless, the conclusions of a statistical inference are a statistical proposition. Some common forms of statistical proposition they point to include:
- Estimates: A particular value that best approximates some parameter of interest
- Confidence interval: an interval constructed using a data set drawn from a population so that, under repeated sampling of such data sets, such intervals would contain the true parameter value with the probability at the stated confidence level
- Credible intervals: A set of values containing, for example, 95 percent of posterior belief
In the end, descriptive statistics are used to describe the data, while inferential statistics are used to infer conclusions and hypotheses about the same information.
Statistical analysis software
Since not everyone is a mathematic genius who is able to easily compute the needed statistics on the mounds of data a company acquires, most organizations use some form of statistical analysis software. The software, which is offered by a number of providers, delivers the specific analysis an organization needs to better their business.
The software is able to quickly and easily generate charts and graphs when conducting descriptive statistics, while at the same time conduct the more sophisticated computations that are required when conducting inferential statistics.
Among some of the more popular statistical analysis software services are IBM's SPSS, SAS, Revolution Analytics' R, Minitab and Stata.