Bayesian Statistics in a Nutshell

Intended for students of psychological and/or organizational sciences

Prepared by Andrew Jebb and Sang Eun Woo
August 27, 2014

Bayesian statistics is an approach to statistical inference (i.e., drawing conclusions about the population through sample data) that is fundamentally different than the conventional frequentist approach. Bayesian methods derive their name from Bayes’ Theorem, a mathematical equation built off of simple probability axioms. In essence, it allows an analyst to calculate any conditional probability of interest. A conditional probability is simply the probability of event A given that event B has occurred. It is a probability that is therefore “conditional” on another event. Statistical analyses are based on a collection of sample data. Therefore, in probability terms, we know that the data have already occurred. Using Bayes’ theorem, we can directly calculate the probability of various things of interest given, or conditional on, this already observed data. It is a highly intuitive approach to statistical inference that allows for direct probability statements regarding things researchers are interested in, such as population values or statistical models.

As we elaborate later, three primary advantages of Bayesian statistics are that it is:
1) A remarkably rich source of information on which to draw conclusions;
2) A natural framework to include previous information;
3) Flexible--e.g., accommodating complex models and small samples.


In general, there are two methods for performing statistical inference: estimation and hypothesis testing. Estimation is a process of mathematically deriving accurate numerical representations of the actual population value (i.e., parameter). In contrast, hypothesis testing involves formally testing competing specific statistical hypotheses—i.e., hypotheses related to the value of a parameter (e.g., the population value is zero or not).

Both the Bayesian and frequentist statistical paradigms have their unique approach to estimation and hypothesis testing. The basis of these differences lies in their philosophical differences about how probability should be conceived. However, one does not have to subscribe to the Bayesian or frequentist notion of probability to use these statistics in practice -- This is an important point because researchers who currently practice frequentist statistics are often unaware of its theoretical foundations.

Due to its view of probability, frequentist estimation tries to locate a single parameter estimate that best fits the data. It is possible (and strongly recommended by many methodologists and the Psychological Sciences journal) to then provide a range of plausible values around that point estimate through the use of a confidence interval, which indicates the precision of the estimate. However, research has shown that this is rarely done (Finch, Cumming & Thomason, 2001; Finch, Cumming, Williams, Palmer, Griffith, Alders, Anderson, & Goodman, 2004). Instead, researchers rely on null hypothesis significance testing (NHST), a formal test of the hypothesis that the actual population value is zero (i.e., “null”). The practical and conceptual problems associated with NHST have been catalogued in a diverse literature spanning decades (e.g., Anderson, Burnham, & Thompson, 2000; Cohen, 1994; Gigerenzer, 2004; Gigerenzer, Krauss, & Vitouch, 2004; Johnson, 1999; Kline, 2004; Kruschke, 2010; Meehl, 1978; Morrison & Henkel, 1970; Rozeboom, 1960; Schmidt, 1996; Simmons, Nelson, and Simonsohn, 2011; Wagenmakers, 2007). Although frequentist estimation can be useful for research (see Cumming, 2014), NHST has become a “mindless ritual” despite this being contrary to its historical origins (Gigerenzer, 2004; Gigerenzer et al., 2004).


Many concerns have been articulated about the deficiencies and problems of NHST (as cited above), which we summarize into four points:

1)  NHST can never provide evidence that the null hypothesis is true; the null can only be rejected, never accepted.

2) When one fails to reject the null hypothesis, nothing can be concluded from the results. This is an impressive waste of researcher time and resources (not to mention sanity).

3) The null hypothesis that a population value (parameter) is precisely “zero” is never actually true; there is always an effect, no matter how small, and indefinitely increasing the statistical power (i.e., sample size) will guarantee a “significant” result. Thus, even before conducting a significance test, the researcher already knows its outcome. This discourages researchers from maintaining honest research practices by looking for ways to strategically inflate their ability to reject the null. (Bakker, van Dijk, and Wicherts [2012] aptly referred to statistics in psychology as a mere “game”; also see O’Boyle, Banks, and Gonzalez-Mule, [in press]).

4) A result that is “statistically significant” (i.e., when the null hypothesis is rejected) does not entail that it is practically significant. In other words, statistical significance is just a way to formally assess whether an estimated effect is actually there or not; it does not actually bear on the research questions that impel scholars.

Interestingly, the version of NHST that is currently practiced is a mixture of different approaches to significance testing that no camp on its own would condone (Gigerenzer, 2004). Unfortunately, it remains nothing less than an institution in the sciences (Orlitzky, 2012). Although it has proved useful in many respects, it is an outdated practice that often leads to spurious (i.e., false) scientific conclusions (Ioannidis, 2005).


In contrast, the Bayesian approach to statistical inference avoids all of these problems. It is a logically sound way to perform statistical inference that is rapidly growing in practically every domain of science. Bayesian statistical inference is done primarily through estimation, with Bayesian hypothesis testing reserved for model selection (see Kass & Raftery, 1995 for a review). Bayesian statistics avoids the myriad problems associated with NHST. It also has significant intrinsic benefits.

The most important benefit is that Bayesian estimation provides a remarkably rich source of information on which to draw conclusions: Each estimated parameter is represented in a probability distribution, where each potential parameter value is probabilistically weighted, allowing the analyst to see how probable each potential parameter value is.

Another substantial advantage of the Bayesian paradigm is that it provides a natural framework to include previous information. This is accomplished through the use of prior distributions for each statistical parameter, which quantify the analyst’s prior certainty (or uncertainty) regarding its possible values. The progression of science is based on the accumulation of research findings. That is, science builds off of itself, and our methods of data analysis should reflect this fact. Therefore, using Bayesian statistics allows researchers toformally integrate what is already known on the topic of interest within present analyses (see Zyphur & Oswald [in press] for information regarding different types of prior distributions).

Finally, Bayesian estimation is great for research where large samples are difficult or impossible to obtain, is more intuitive than frequentist methods, and can accommodate the increasingly complex models seen in contemporary research (for a more complete list of advantages, see Kruschke, Aguinis, & Joo, 2012, pp.730-739).


In spite of its advantages, Bayesian statistics requires that the analyst learn more about probability and statistical theory (although this should be considered an advantage). Also, satisfactory software implementation of these analyses is still in development. By far, the most popular program for Bayesian estimation is WinBUGS/OpenBUGS—a great and highly-flexible open-source program. However, it requires notable programming knowledge and a very in-depth understanding of the Bayesian modeling process. BugsXLA is a newer software tool for Bayesian estimation that is much more intuitive for social scientists. Instead of learning software coding, one sets up the model using a very simple GUI (graphical user interface) in Excel. The program then automatically uses the WinBUGS engine to conduct the analysis and imports the results directly back into Excel.

We have used BugsXLA as the focal software in our paper, “A Bayesian Primer for the Organizational Sciences: The “Two Sources” and an Introduction to BugsXLA” (Jebb & Woo, 2015), which we hope will encourage our fellow scientists to begin exploring and using Bayesian methods. Click here for the Excel data for running the analyses. Of course, to run the analysis, you will need to install BugsXLA. You will also need to install WinBUGS.


There are a lot of great resources for those interested in learning more about Bayesian statistics. In our opinion, the two most accessible texts for social scientists are Scott Lynch’s (2007) Introduction to applied Bayesian statistics and estimation for social scientists andJohn Kruschke’s (2011) Doing Bayesian data analysis: A tutorial with R and BUGS. We recommend that these be read in complement, as some concepts are explained more intuitively in one than in the other.

For those with a slightly stronger background in statistics, we recommend Gelman, Carlin, Stern, and Rubin (2004). For many, it remains the “classic” textbook on Bayesian modeling. It is our experience that other books are geared toward a statistics audience and may not be accessible to social scientists.  

Last but not least, there are also many good journal articles delineating Bayesian methods, such as Kruschke (2013), Kruschke et al. (2012), and Zyphur and Oswald (2015).

In sum, we believe that research in every domain has much to gain from an increased use in Bayesian data analysis. We also believe that these methods have been made much more accessible in recent years and hope that their use will continue to spread for many years to come.



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