True story: A colleague and I used to joke that our findings were “robust to coding errors” because often we’d find bugs in the little programs we’d written—hey, it happens!—but when we fixed things it just about never changed our main conclusions. If the reason you’re doing it is to buttress a conclusion you already believe, to respond to referees in a way that will allow you to keep your substantive conclusions unchanged, then all sorts of problems can arise. small data sets) – so one had better avoid the mistake made by economists of trying to copy classical mechanics – where it might be profitable to look for ideas, and this has of course been done, is statistical mechanics). is there something shady going on? Among other things, Leamer shows that regressions using different sets of control variables, both of which might be deemed reasonable, can lead to different substantive interpretations (see Section V.). The idea is as Andrew states – to make sure your conclusions hold under different assumptions. ANSI and IEEE have defined robustness as the degree to which a system or component can function correctly in the presence of invalid inputs or stressful environmental conditions. Drives me nuts as a reviewer when authors describe #2 analyses as “robustness tests”, because it minimizes #2’s (huge) importance (if the goal is causal inference at least). That is, p-values are a sort of measure of robustness across potential samples, under the assumption that the dispersion of the underlying population is accurately reflected in the sample at hand. Of course, there is nothing novel about this point of view, and there has been a lot of work based on it. It’s better than nothing. ), I’ve also encountered “robust” used in a third way: For example, if a study about “people” used data from Americans, would the results be the same of the data were from Canadians? But the usual reason for a robustness check, I think, is to demonstrate that your main analysis is OK. In this test, the bottom temperature starts below the reference value. Ad hoc testing: a testing phase where the tester tries to "break" the system by randomly In statistics, the term robust or robustness refers to the strength of a statistical model, tests, and procedures according to the specific conditions of the statistical analysis a study hopes to achieve.Given that these conditions of a study are met, the models can be verified to be true through the use of mathematical … But it isn’t intended to be. obvious typo at the end: “some of these checks” not “some these these checks”. The term "robustness testing… Software Testing Metrics are the quantitative measures used to estimate the progress, quality, productivity and health of the software testing process. I think this would often be better than specifying a different prior that may not be that different in important ways. However, robustness generally comes at the cost of power, because either less information from the input is used, or more … Sometimes this makes sense. Third, for me robustness subsumes the sort of testing that has given us p-values and all the rest. You paint an overly bleak picture of statistical methods research and or published justifications given for methods used. 2. Another social mechanism is bringing the wisdom of “gray hairs” to bear on an issue. There are other routes to getting less wrong Bayesian models by plotting marginal priors or analytically determining the impact of the prior on the primary credible intervals. It’s typically performed under the assumption that whatever you’re doing is just fine, and the audience for the robustness check includes the journal editor, referees, and anyone else out there who might be skeptical of your claims. If required should be easy to divide into different modules for testing. In field areas where there are high levels of agreement on appropriate methods and measurement, robustness testing need not be very broad. And that is well and good. However, whil the analogy with physical stability is useful as a starting point, it does not seem to be useful in guiding the formulation of the relevant definitions (I think this is a point where many approaches go astray). That a statistical analysis is not robust with respect to the framing of the model should mean roughly that small changes in the inputs cause large changes in the outputs. Many of these are equivalent, and some are used to define a specific type of robustness testing. In situations where missingness is plausibly strongly related to the unobserved values, and nothing that has been observed will straighten this out through conditioning, a reasonable approach is to develop several different models of the missing data and apply them. The variability of the effect across these cuts is an important part of the story; if its pattern is problematic, that’s a strike against the effect, or its generality at least. The most extreme is the pizzagate guy, where people keep pointing out major errors in his data and analysis, and he keeps saying that his substantive conclusions are unaffected: it’s a big joke. This should give you an idea of how successful the robust regression was.Best wishes. Sensitivity to input parameters is fine, if those input parameters represent real information that you want to include in your model it’s not so fine if the input parameters are arbitrary. As with all epiphanies of the it-all-comes-down-to sort, I may be shoehorning concepts that are better left apart. Robustness testing is known by many different names. I blame publishers. Is this selection bias? Maybe what is needed are cranky iconoclasts who derive pleasure from smashing idols and are not co-opted by prestige. However, as technology improved, software became more complex and software projects grew larger. +1 on both points. I would suggest comparing the residual analysis for the OLS regression with that from the robust regression. Or Andrew’s ordered logit example above. You can be more or less robust across measurement procedures (apparatuses, proxies, whatever), statistical models (where multiple models are plausible), and—especially—subsamples. Of course these checks can give false re-assurances, if something is truly, and wildly, spurious then it should be expected to be robust to some these these checks (but not all). Well, that occurred to us too, and so we did … and we found it didn’t make a difference, so you don’t have to be concerned about that.” These types of questions naturally occur to authors, reviewers, and seminar participants, and it is helpful for authors to address them. Yes, I’ve seen this many times. It is quite common, at least in the circles I travel in, to reflexively apply multiple imputation to analyses where there is missing data. When the more complicated model fails to achieve the needed results, it forms an independent test of the unobservable conditions for that model to be more accurate. This experiment highlights the reliability and robustness that compact, modular instruments can offer laboratories that require workflow flexibility. Robustness testing … Your experience may vary. I think this is related to the commonly used (at least in economics) idea of “these results hold, after accounting for factors X, Y, Z, …). Machine learning is a sort of subsample robustness, yes? This may be a valuable insight into how to deal with p-hacking, forking paths, and the other statistical problems in modern research. such software. Adhoc testing: Ad-hoc testing is quite opposite to the formal testing… Perhaps not quite the same as the specific question, but Hampel once called robust statistics the stability theory of statistics and gave an analogy to stability of differential equations. Yes, as far as I am aware, “robustness” is a vague and loosely used term by economists – used to mean many possible things and motivated for many different reasons. What I said is that it’s a problem to be using a method whose goal is to demonstrate that your main analysis is OK. It can be useful to have someone with deep knowledge of the field share their wisdom about what is real and what is bogus in a given field. Adaptable to other products with which it needs interaction. Should be flexible enough to modify. One dimension is what you’re saying, that it’s good to understand the sensitivity of conclusions to assumptions. and influential … First, robustness is not binary, although people (especially people with econ training) often talk about it that way. Discussion of robustness is one way that dispersed wisdom is brought to bear on a paper’s analysis. In the latter category, robustness testing describes a class of approaches that evaluates the degree to which a sys-tem or component can function correctly in the presence of invalid inputs or stressful environmental conditions. Reusability TestNG is a testing framework developed in the lines of JUnit and NUnit, however it introduces some new functionalities that make it more powerful and easier to use. (Yes, the null is a problematic benchmark, but a t-stat does tell you something of value.). Or just an often very accurate picture ;-). The official reason, as it were, for a robustness check, is to see how your conclusions change when your assumptions change. And there are those prior and posterior predictive checks. And from this point of view, replication is also about robustness in multiple respects. etc. As discussed frequently on this blog, this “accounting” is usually vague and loosely used. Unfortunately, upstarts can be co-opted by the currency of prestige into shoring up a flawed structure. No. . keeping the data set fixed). This tutorial provides a good understanding on TestNG framework needed to test an enterprise-level application to deliver it with robustness and reliability. Test Strategy is also known as test approach defines how testing would be carried out. I like robustness checks that act as a sort of internal replication (i.e. For example, maybe you have discrete data with many categories, you fit using a continuous regression model which makes your analysis easier to perform, more flexible, and also easier to understand and explain—and then it makes sense to do a robustness check, re-fitting using ordered logit, just to check that nothing changes much. Vulnerability Testing - checklist: Verify the strength of the password as it provides some degree of security. You do the robustness check and you find that your result persists. Robustness testing. Is it a statistically rigorous process? Test approach has two techniques: Proactive - An approach in which the test design process is initiated as early as possible in order to find and fix the defects before the build is created. Of course the difficult thing is giving operational meaning to the words small and large, and, concomitantly, framing the model in a way sufficiently well-delineated to admit such quantifications (however approximate). Software development now necessitated the presence of a team, which could prepare detailed plans and designs, carry out testing… I ask this because robustness checks are always just mentioned as a side note to presentations (yes we did a robustness check and it still works!). While performing the manual testing on any application, we do not need any specific knowledge of any testing tool, rather than have a proper understanding of the product so we can easily prepare the test document. Not much is really learned from such an exercise. . The other names of structural testing includes clear box testing, open box testing, logic driven testing or path driven testing. In many papers, “robustness test” simultaneously refers to: Yet many people with papers that have very weak inferences that struggle with alternative arguments (i.e., have huge endogeneity problems, might have causation backwards, etc) often try to just push the discussions of those weaknesses into an appendix, or a footnote, so that they can be quickly waved away as a robustness test. We can generate 19 test cases from both variables X, Y, and Z. 1.0 Introduction The practice of testing software has become one of the most important aspects of the process of … There are a total of 3 variables X, Y and Z. That is, p-values are a sort of measure of robustness across potential samples, under the assumption that the dispersion of the underlying population is accurately reflected in the sample at hand. “Naive” pretty much always means “less techie”. 1 is for nominal. 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