Practical guidelines for testing statistical software

It will help you find any errors that you missed during cleaning like the 99s you forgot to declare as missing values. The first thing to do is univariate descriptives, or better yet, graphs. Values with a huge number of points. Surprising values that are generally much higher or with less variation than you expected. Once you put these variables in the model, they may behave funny. You also need to understand how each potential predictor relates, on its own, to the outcome and to every other predictor.

Because the regression coefficients are marginal results see 1 , knowing the bivariate relationships among variables will give you insight into why certain variable lose significance in the bigger model. I personally find that in addition to correlations or crosstabs , scatterplots of the relationship are extremely informative.

This is where you can see if linear relationships are plausible or if you need to deal with nonlinearity in some way. By building the models within those sets first, we were able to see how related variables worked together and then what happened once we put them together. For example, think about a model that predicts binge drinking in college students. Potential sets of variables include:. Often, the variables within a set are correlated, but not so much across sets.

By building each set separately first, you can build theoretically meaningful models with a solid understanding of how the pieces fit together. Look at the coefficients. Look at R-squared. Did it change? How much do coefficients change from a model with control variables to one without?

The interpretation of the interaction is only possible if the component term is in the model. Keep the focus on your destination— the research question.

The testers should have a destructive approach towards the product. Developers can perform unit testing and integration testing but software testing should be done by the testing team. In other words, there is no way to prove that the software is free of errors even after making a number of test cases. Start as early as possible: Testing should always starts parallelly alongside the requirement analysis process.

This is crucial in order to avoid the problem of defect migration. It is important to determine the test objects and scope as early as possible. Prioritize sections: If there are certain critical sections, then it should be ensured that these sections are tested with the highest priority and as early as possible.

The time available is limited: Testing time for software is limited. It must be kept in mind that the time available for testing is not unlimited and that an effective test plan is very crucial before starting the process of testing.

There should be some criteria to decide when to terminate the process of testing. This criterion needs to be decided beforehand. For instance, when the system is left with an acceptable level of risk or according to timelines or budget constraints. Salvatier, J. Probabilistic programming in Python using pyMC. PeerJ Computer Science , 3 2 , e Bayes factor design analysis: Planning for compelling evidence.

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A cautionary note on estimating effect size. Retrieved from psyarxiv. Download references. We thank Dr. Simons, two anonymous reviewers, and the editor for comments on an earlier draft. E-mail may be sent to johnnydoorn gmail. Evans, Quentin F. Gronau, Julia M.

Nyenrode Business University, Breukelen, Netherlands. You can also search for this author in PubMed Google Scholar. JvD wrote the main manuscript. All authors reviewed the manuscript and provided feedback. Correspondence to Johnny van Doorn. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material.

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Psychon Bull Rev 28, — Download citation. Published : 09 October Issue Date : June Anyone you share the following link with will be able to read this content:. Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative. Skip to main content. Search SpringerLink Search. Download PDF. Abstract Despite the increasing popularity of Bayesian inference in empirical research, few practical guidelines provide detailed recommendations for how to apply Bayesian procedures and interpret the results.

Table 1 A summary of the guidelines for the different stages of a Bayesian analysis, with a focus on analyses conducted in JASP. Full size table. Stage 1: Planning the analysis Specifying the goal of the analysis. Box 1. Hypothesis testing The principled approach to Bayesian hypothesis testing is by means of the Bayes factor e.

Stage 2: Executing the analysis Before executing the primary analysis and interpreting the outcome, it is important to confirm that the intended analyses are appropriate and the models are not grossly misspecified for the data at hand. Full size image. Stage 3: Interpreting the results With the analysis outcome in hand, we are ready to draw conclusions. Stage 4: Reporting the results For increased transparency, and to allow a skeptical assessment of the statistical claims, we recommend to present an elaborate analysis report including relevant tables, figures, assumption checks, and background information.

Limitations and challenges The Bayesian toolkit for the empirical social scientist still has some limitations to overcome. Concluding comments We have attempted to provide concise recommendations for planning, executing, interpreting, and reporting Bayesian analyses.

Notes 1. This confusion does not arise for the rarely reported unconditional distributions see Box 3. References Andrews, M. Google Scholar Anscombe, F. Google Scholar Appelbaum, M. Google Scholar Berger, J. Google Scholar Carpenter, B. Google Scholar Clyde, M.

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Google Scholar Wagenmakers, E. Statistical testing of software is here defined as testing in which the test cases are produced by a random process meant to produce different test cases with the same probabilities with which they would arise in actual use of the software.

Statistical testing of software has these main advantages: for the purpose of reliability assessment and product acceptance, it supports directly estimates of reliability, and thus decisions on whether the software is ready for delivery or for use in a specific system.

This feature is unique to statistical testing; for the purpose of improving the software, it tends to discover defects which would cause failures with the higher frequencies before those that would cause less frequent failures, thus focusing correction efforts in the most cost-effective way and delivering better software for a given debugging effort.