Image by Liz Sullivan, Creative Commons. Source: Wikimedia
An all too common approach to modeling in data science is to throw all possible variables at a modeling procedure and “let the algorithm sort it out.” This is tempting when you are not sure what are the true causes or predictors of the phenomenon you are interested in, but it presents dangers, too. Very wide data sets are computationally difficult for some modeling procedures; and more importantly, they can lead to overfit models that generalize poorly on new data. In extreme cases, wide data can fool modeling procedures into finding models that look good on training data, even when that data has no signal. We showed some examples of this previously in our “Bad Bayes” blog post.
Having worked in finance I am a public fan of the Sharpe ratio. I have written about this here and here.
One thing I have often forgotten (driving some bad analyses) is: the Sharpe ratio isn’t appropriate for models of repeated events that already have linked mean and variance (such as Poisson or Binomial models) or situations where the variance is very small (with respect to the mean or expectation). These are common situations in a number of large scale online advertising problems (such as modeling the response rate to online advertisements or email campaigns).
A/B tests are one of the simplest reliable experimental designs.
Controlled experiments embody the best scientific design for establishing a causal relationship between changes and their influence on user-observable behavior.
“Practical guide to controlled experiments on the web: listen to your customers not to the HIPPO” Ron Kohavi, Randal M Henne, and Dan Sommerfield, Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, 2007 pp. 959-967.
The ideas is to test a variation (called “treatment” or “B”) in parallel with continuing to test a baseline (called “control” or “A”) to see if the variation drives a desired effect (increase in revenue, cure of disease, and so on). By running both tests at the same time it is hoped that any confounding or omitted factors are nearly evenly distributed between the two groups and therefore not spoiling results. This is a much safer system of testing than retrospective studies (where we look for features from data already collected).
Some researchers (in both science and marketing) abuse a slavish view of p-values to try and falsely claim credibility. The incantation is: “we achieved p = x (with x ≤ 0.05) so you should trust our work.” This might be true if the published result had been performed as a single project (and not as the sole shared result in longer series of private experiments) and really points to the fact that even frequentist significance is a subjective and intensional quantity (an accusation usually reserved for Bayesian inference). In this article we will comment briefly on the negative effect of un-reported repeated experiments and what should be done to compensate. Continue reading Drowning in insignificance
It occurred to us recently that we don’t have any articles about Bayesian approaches to statistics here. I’m not going to get into the “Bayesian versus Frequentist” war; in my opinion, which style of approach to use is less about philosophy, and more about figuring out the best way to answer a question. Once you have the right question, then the right approach will naturally suggest itself to you. It could be a frequentist approach, it could be a bayesian one, it could be both — even while solving the same problem.
Let’s take the example that Bayesians love to hate: significance testing, especially in clinical trial style experiments. Clinical trial experiments are designed to answer questions of the form “Does treatment X have a discernible effect on condition Y, on average?” To be specific, let’s use the question “Does drugX reduce hypertension, on average?” Assuming that your experiment does show a positive effect, the statistical significance tests that you run should check for the sorts of problems that John discussed in our previous article, Worry about correctness and repeatability, not p-values: What are the chances that an ineffective drug could produce the results that I saw? How likely is it that another researcher could replicate my results with the same size trial?
We can argue about whether or not the question we are answering is the correct question — but given that it is the question, the procedure to answer it and to verify the statistical validity of the results is perfectly appropriate.
So what is the correct question? From your family doctor’s viewpoint, a clinical trial answers the question “If I prescribe drugX to all my hypertensive patients, will their blood pressure improve, on average?” That isn’t the question (hopefully) that your doctor actually asks, though possibly your insurance company does. Your doctor should be asking “If I prescribe drugX to this patient, the one sitting in my examination room, will the patient’s blood pressure improve?” There is only one patient, so there is no such thing as “on average.”
In data science work you often run into cryptic sentences like the following:
Age adjusted death rates per 10,000 person years across incremental thirds of muscular strength were 38.9, 25.9, and 26.6 for all causes; 12.1, 7.6, and 6.6 for cardiovascular disease; and 6.1, 4.9, and 4.2 for cancer (all P < 0.01 for linear trend).
(From “Association between muscular strength and mortality in men: prospective cohort study,” Ruiz et. al. BMJ 2008;337:a439.)
The accepted procedure is to recognize “p” or “p-value” as shorthand for “significance,” keep your mouth shut and hope the paper explains what is actually claimed somewhere later on. We know the writer is claiming significance, but despite the technical terminology they have not actually said which test they actually ran (lm(), glm(), contingency table, normal test, t-test, f-test, g-test, chi-sq, permutation test, exact test and so on). I am going to go out on a limb here and say these type of sentences are gibberish and nobody actually understands them. From experience we know generally what to expect, but it isn’t until we read further we can precisely pin down what is actually being claimed. This isn’t the authors’ fault, they are likely good scientists, good statisticians, and good writers; but this incantation is required by publishing tradition and reviewers.
We argue you should worry about the correctness of your results (how likely a bad result could look like yours, the subject of frequentist significance) and repeatability (how much variance is in your estimation procedure, as measured by procedures like the bootstrap). p-values and significance are important in how they help structure the above questions.
The legitimate purpose of technical jargon is to make conversations quicker and more precise. However, saying “p” is not much shorter than saying “significance” and there are many different procedures that return p-values (so saying “p” does not limit you down to exactly one procedure like a good acronym might). At best the savings in time would be from having to spend 10 minutes thinking which interpretation of significance is most approbate to the actual problem at hand versus needing a mere 30 seconds to read about the “p.” However, if you don’t have 10 minutes to consider if the entire result a paper is likely an observation artifact due to chance or noise (the subject of significance) then you really don’t care much about the paper.
In our opinion “p-values” have degenerated from a useful jargon into a secretive argot. We are going to discuss thinking about significance as “worrying about correctness” (a fundamental concern) instead of as a cut and dried statistical procedure you should automate out of view (uncritically copying reported p’s from fitters). Yes “p”s are significances, but there is no reason to not just say what sort of error you are claiming is unlikely. Continue reading Worry about correctness and repeatability, not p-values
In our article What is a large enough random sample? we pointed out that if you wanted to measure a proportion to an accuracy “a” with chance of being wrong of “d” then a idea was to guarantee you had a sample size of at least:
This is the central question in designing opinion polls or running A/B tests. This estimate comes from a quick application of Hoeffding’s inequality and because it has a simple form it is possible to see that accuracy is very expensive (to halve the size of difference we are trying to measure we have to multiply the sample size by four) and the cheapness of confidence (increases in the required confidence or significance of a result cost only moderately in sample size).
However, for high-accuracy situations (when you are trying to measure two effects that are very close to each other) suggesting a sample size that is larger than is strictly necessary (as we are using an bound, not an exact formula for the required sample size). As a theorist or a statistician we like to error on the side of too large a sample (guaranteeing reliability), but somebody who is paying for each entry in a poll would want a smaller size.
A lot of gamers, after missing a 80% chance of success shot, start asking if the game’s pseudo random number generator is fair. Is the game really rolling the dice as stated, or is it cheating? Of course the matching question is: are player memories at all fair; would they remember the other 4 out of 5 times they made such a shot?
In the previous installment of the Statistics to English Translation, we discussed the technical meaning of the term ”significant”. In this installment, we look at how significance is calculated. This article will be a little more technically detailed than the last one, but our primary goal is still to help you decipher statements about significance in research papers: statements like “
As in the last article, we will concentrate on situations where we want to test the difference of means. You should read that previous article first, so you are familiar with the terminology that we use in this one.