may be in the same situation — or, you may find yourself learning so-called open-source statistical

software packages. The most common are R and Python. This software is free to the user and

downloadable online because it is built by the user community, not a company.

As the Internet evolved, more options became available for statistical software. In addition to the

existing stand-alone applications described earlier, specialized statistical apps were developed that

only perform one or a small collection of specific statistical functions (such as G*Power and PS,

which are for calculating sample sizes). Similarly, web-based online calculators were developed,

which are typically programmed to do one particular function (such as calculate a chi-square statistic

and p value from counts of data, as described in Chapter 12). Some web pages feature a collection of

such calculators.

Comparing Commercial to Open-Source Software

Before 2010, if an organization performed statistical analysis as part of its core function, it needed to

purchase commercial statistical software like SAS or SPSS. Advantages of implementing commercial

software include the ability to perform many statistical functions, technical support from the software

company, and the expectation that the software will remain in use in the future as the company

continues to support and upgrade it.

However, organizations today are hesitant to adopt commercial software when they can instead use

open-source software like R or Python. Admittedly, even though it is free of charge, there are many

downsides to open-source software. First, you need to hire analysts who know how to use it so well

that they can figure out what to do when there’s a problem because open-source software does not

have tech support. Next, you need to hire a lot more analysts than you would with commercial software

because a lot of their work will be in trying to customize the software for your use and keep it updated

so that your organization runs smoothly.

So, why are new organizations today hesitant to adopt commercial software when open-source

software has so many downsides? The main reason is that the old advantages of commercial software

are not as true anymore. SAS and SPSS are expensive programs, but they have much of the same

functionality as open-source R and Python, which are free. In some cases, analysts prefer the open-

source application to the commercial application because they can customize it more easily to their

setting. Also, it is not clear that commercial software is innovating ahead of open-source software.

Organizations do not want to get entangled with expensive commercial software that eventually starts

to perform worse than free open-source alternatives!

As a result, many organizations use both commercial and open-source statistical software in

integrated application pipelines. Therefore, it is important to be comfortable evaluating and

using various commercial software, even if open-source options are becoming more popular.

Checking Out Commercial Software

In the following sections, we discuss the most popular commercial statistical software available