Rioja vs Ribera

Frequentist vs Bayesian statistics

This is one of the typical debates that one can have with a brother-in-law during a family dinner: whether the wine from Ribera is better than that from Rioja, or vice versa. In the end, as always, the brother-in-law will be (or will want to be) right, which will not prevent us from trying to contradict him. Of course, we must make good arguments to avoid falling into the same error, in my humble opinion, in which some fall when participating in another classic debate, this one from the less playful field of epidemiology: Frequentist vs. Bayesian statistics?

And these are the two approaches that we can use when dealing with a research problem.

Some previous definitions

Frequentist statistics, the best known and to which we are most accustomed, is the one that is developed according to the classic concepts of probability and hypothesis testing. Thus, it is about reaching a conclusion based on the level of statistical significance and the acceptance or rejection of a working hypothesis, always within the framework of the study being carried out. This methodology forces to stabilize the decision parameters a priori, which avoids subjectivities regarding them.

The other approach to solving problems is that of Bayesian statistics, which is increasingly fashionable and, as its name suggests, is based on the probabilistic concept of Bayes’ theorem. Its differentiating feature is that it incorporates external information into the study that is being carried out, so that the probability of a certain event can be modified by the previous information that we have on the event in question. Thus, the information obtained a priori is used to establish an a posteriori probability that allows us to make the inference and reach a conclusion about the problem we are studying.

This is another difference between the two approaches: while Frequentist statistics avoids subjectivity, Bayesian’s one introduces a subjective (but not capricious) definition of probability, based on the researcher’s conviction, to make judgments about a hypothesis.

Bayesian statistics is not really new. Thomas Bayes’ theory of probability was published in 1763, but experiences a resurgence from the last third of the last century. And as usually happens in these cases where there are two alternatives, supporters and detractors of both methods appear, which are deeply involved in the fight to demonstrate the benefits of their preferred method, sometimes looking more for the weaknesses of the opposite than for their own strengths.

And this is what we are going to talk about in this post, about some arguments that Bayesians use on some occasion that, one more time in my humble opinion, take more advantage misuses of Frequentist statistics by many authors, than of intrinsic defects of this methodology.

A bit of history

We will start with a bit of history.

The history of hypothesis testing begins back in the 20s of the last century, when the great Ronald Fisher proposed to value the working hypothesis (of absence of effect) through a specific observation and the probability of observing a value equal or greater than the observed result. This probability is the p-value, so sacred and so misinterpreted, that it does not mean more than that: the probability of finding a value equal to or more extreme than that found if the working hypothesis were true.

In summary, the p that Fisher proposed is nothing short of a measure of the discrepancy that could exist between the data found and the hypothesis of work proposed, the null hypothesis (H0).

Almost a decade later, the concept of alternative hypothesis (H1) was introduced, which did not exist in Fisher’s original approach, and the reasoning is modified based on two error rates of false positive and negative:

  1. Alpha error (type 1 error): probability of rejecting the null hypothesis when, in fact, it is true. It would be the false positive: we believe we detect an effect that, in reality, does not exist.
  2. Beta error (type 2 error): it is the probability of accepting the null hypothesis when, in fact, it is false. It is the false negative: we fail to detect an effect that actually exists.

Thus, we set a maximum value for what seems to us the worst case scenario, which is detecting a false effect, and we choose a “small” value. How small is it? Well, by convention, 0.05 (sometimes 0.01). But, I repeat, it is a value chosen by agreement (and there are those who say that it is capricious, because 5% reminds them the fingers of the hand, which are usually 5).

Thus, if p <0.05, we reject H0 in favor of H1. Otherwise, we accept H0, the hypothesis of no effect. It is important to note that we can only reject H0, never demonstrate it in a positive way. We can demonstrate the effect, but not its absence.

Everything said so far seems easy to understand: the frequentist method tries to quantify the level of uncertainty of our estimate to try to draw a conclusion from the results. The problem is that p, which is nothing more than a way to quantify this uncertainty, is sacralized and misinterpreted too often, which is used to their advantage (if I may say so) by opponents of the method to try to expose its weaknesses.

One of the major flaws attributed to the frequentist method is the dependence of the p-value on the sample size. Indeed, the value of p can be the same with a small effect size in a large sample as with a large effect size in a small sample. And this is more important than it may seem at first, since the value that will allow us to reach a conclusion will depend on a decision exogenous to the problem we are examining: the chosen sample size.

Here would be the benefit of the Bayesian method, in which larger samples would serve to provide more and more information about the study phenomenon. But I think this argument is based on a misunderstanding of what an adequate sample is. I am convinced, the more is not always the better.

We start with the debate

Another great man, David Sackett, said that “too small samples can be used to prove nothing; samples that are too large can be used to prove nothing ”. The problem is that, in my opinion, a sample is neither large nor small, but sufficient or insufficient to demonstrate the existence (or not) of an effect size that is considered clinically important.

And this is the heart of the matter. When we want to study the effect of an intervention we must, a priori, define what effect size we want to detect and calculate the necessary sample size to be able to do it, as long as the effect exists (something that we desire when we plan the experiment, but that we don’t know a priori) . When we do a clinical trial we are spending time and money, in addition to subjecting participants to potential risk, so it is important to include only those necessary to try to prove the clinically important effect. Including the necessary participants to reach the desired p <0.05, in addition to being uneconomic and unethical, demonstrates a lack of knowledge about the true meaning of p-value and sample size.

This misinterpretation of the p-value is also the reason that many authors who do not reach the desired statistical significance allow themselves to affirm that with a larger sample size they would have achieved it. And they are right, they would have reached the desired p <0.05, but they again ignore the importance of clinical significance versus statistical significance.

When the sample size to detect the clinically important effect is calculated a priori, the power of the study is also calculated, which is the probability of detecting the effect if it actually exists. If the power is greater than 80-90%, the values admitted by convention, it does not seem correct to say that you do not have enough sample. And, of course, if you have not calculated the power of the study before, you should do it before affirming that you have no results due to shortness of sample.

Another argument against the frequentist method and in favor of the Bayesian’s says that hypothesis testing is a dichotomous decision process, in which a hypothesis is rejected or accepted such as you rejects or accepts an invitation to the wedding of a distant cousin you haven’t seen for years.

Well, if they previously forgot about clinical significance, those who affirm this fact forget about our beloved confidence intervals. The results of a study should not be interpreted solely on the basis of the p-value. We must look at the confidence intervals, which inform us of the precision of the result and of the possible values that the observed effect may have and that we cannot further specify due to the effect of chance. As we saw in a previous post, the analysis of the confidence intervals can give us clinically important information, sometimes, although the p is not statistically significant.

More arguments

Finally, some detractors of the frequentist method say that the hypothesis test makes decisions without considering information external to the experiment. Again, a misinterpretation of the value of p.

As we already said in a previous post, a value of p <0.05 does not mean that H0 is false, nor that the study is more reliable, or that the result is important (even though the p has six zeros). But, most importantly for what we are discussing now, it is false that the value of p represents the probability that H0 is false (the probability that the effect is real).

Once our results allow us to affirm, with a small margin of error, that the detected effect is real and not random (in other words, when the p is statistically significant), we can calculate the probability that the effect is “real”. And for this, Oh, surprise! we will have to calibrate the value of p with the value of the basal probability of H0, which will be assigned by the researcher based on her knowledge or previous available data (which is still a Bayesian approach).

As you can see, the assessment of the credibility or likelihood of the hypothesis, one of the differentiating characteristics of the Bayesian’s approach, can also be used if we use frequentist methods.

We’re leaving…

And here we are going to leave it for today. But before finishing I would like to make a couple of considerations.

First, in Spain we have many great wines throughout our geography, not just Ribera or Rioja. For no one to get offended, I have chosen these two because they are usually the ones asked by the brothers-in-law when they come to have dinner at home.

Second, do not misunderstand me if it may have seemed to you that I am an advocate of frequentist statistics against Bayesian’s. Just as when I go to the supermarket I feel happy to be able to buy wine from various designations of origin, in research methodology I find it very good to have different ways of approaching a problem. If I want to know if my team is going to win a match, it doesn’t seem very practical to repeat the match 200 times to see what average results come out. It  would be better to try to make an inference taking into account the previous results.

And that’s all. We have not gone into depth in what we have commented at the end on the real probability of the effect, somehow mixing both approaches, frequentist’s and Bayesian’s. The easiest way, as we saw in a previous post, is to use a Held’s nomogram. But that is another story…

Worshipped, but misunderstood

Statistics wears most of us who call ourselves “clinicians” out. The knowledge on the subject acquired during our formative years has long lived in the foggy world of oblivion. We vaguely remember terms such as probability distribution, hypothesis contrast, analysis of variance, regression … It is for this reason that we are always a bit apprehensive when we come to the methods section of scientific articles, in which all these techniques are detailed that, although they are known to us, we do not know with enough depth to correctly interpret their results.

Fortunately, Providence has given us a lifebelt: our beloved and worshipped p. Who has not felt lost with a cumbersome description of mathematical methods to finally breathe a sigh of relieve when finding the value of p? Especially if the p is small and has many zeros.

The problem with p is that, although it is unanimously worshipped, it is also mostly misunderstood. Its value is, very often, misinterpreted. And this is so because many of us harbor misconceptions about what the p-value really means.

Let’s try to clarify it.

Whenever we want to know something about a variable, the effect of an exposure, the comparison of two treatments, etc., we will face the ubiquity of random: it is everywhere and we can never get rid of it, although we can try to limit it and, of course, try to measure its effect.

Let’s give an example to understand it better. Suppose we are doing a clinical trial to compare the effect of two diets, A and B, on weight gain in two groups of participants. Simplifying, the trial will have one of three outcomes: those of diet A gain more weight, those of diet B gain more weight, both groups gain equal weight (there could even be a fourth: both groups lose weight). In any case, we will always obtain a different result, just by chance (even if the two diets are the same).

Imagine that those in diet A put on 2 kg and those in diet B, 3 kg. Is it more fattening the effect of diet B or is the difference due to chance (chosen samples, biological variability, inaccuracy of measurements, etc.)? This is where our hypothesis contrast comes in.

When we are going to do the test, we start from the hypothesis of equality, of no difference in effect (the two diets induce the same increment of weight). This is what we call the null hypothesis (H0) that, I repeat it to keep it clear, we assume that it is the real one. If the variable we are measuring follows a known probability distribution (normal, chi-square, Student’s t, etc.), we can calculate the probability of presenting each of the values of the distribution. In other words, we can calculate the probability of obtaining a result as different from equality as we have obtained, always under the assumption of H0.

That is the p-value: the probability that the difference in the result observed is due to chance. By agreement, if that probability is less than 5% (0.05) it will seem unlikely that the difference is due to chance and we will reject H0, the equality hypothesis, accepting the alternative hypothesis (Ha) that, in this example, will say that one diet better than the other. On the other hand, if the probability is greater than 5%, we will not feel confident enough to affirm that the difference is not due to chance, so we DO NOT reject H0 and we keep with the hypothesis of equal effects: the two diets are similar.

Keep in mind that we always move in the realm of probability. If p is less than 0.05 (statistically significant), we will reject H0, but always with a probability of committing a type 1 error: take for granted an effect that, in reality, does not exist (a false positive). On the other hand, if p is greater than 0.05, we keep with H0 and we say that there is no difference in effect, but always with a probability of committing a type 2 error: not detecting an effect that actually exists (false negative).

We can see, therefore, that the value of p is somewhat simple from the conceptual point of view. However, there are a number of common errors about what p-value represents or does not represent. Let’s try to clarify them.

It is false that a p-value less than 0.05 means that the null hypothesis is false and a p-value greater than 0.05 that the null hypothesis is true. As we have already mentioned, the approach is always probabilistic. The p <0.05 only means that, by agreement, it is unlikely that H0 is true, so we reject it, although always with a small probability of being wrong. On the other hand, if p> 0.05, it is also not guaranteed that H0 is true, since there may be a real effect that the study does not have sufficient power to detect.

At this point we must emphasize one fact: the null hypothesis is only falsifiable. This means that we can only reject it (with which we keep with Ha, with a probability of error), but we can never affirm that it is true. If p> 0.05 we cannot reject it, so we will remain in the initial assumption of equality of effect, which we cannot demonstrate in a positive way.

It is false that p-value is related to the reliability of the study. We can think that the conclusions of the study will be more reliable the lower the value of p, but it is not true either. Actually, the p-value is the probability of obtaining a similar value by chance if we repeat the experiment in the same conditions and it not only depends on whether the effect we want to demonstrate exists or not. There are other factors that can influence the magnitude of the p-value: the sample size, the effect size, the variance of the measured variable, the probability distribution used, etc.

It is false that p-value indicates the relevance of the result. As we have already repeated several times, p-value is only the probability that the difference observed is due to chance. A statistically significant difference does not necessarily have to be clinically relevant. Clinical relevance is established by the researcher and it is possible to find results with a very small p that are not relevant from the clinical point of view and vice versa, insignificant values that are clinically relevant.

It is false that p-value represents the probability that the null hypothesis is true. This belief is why, sometimes, we look for the exact value of p and do not settle for knowing only if it is greater or less than 0.05. The fault of this error of concept is a misinterpretation of conditional probability. We are interested in knowing what is the probability that H0 is true once we have obtained some results with our test. Mathematically expressed, we want to know P (H0 | results). However, the value of p gives us the probability of obtaining our results under the assumption that the null hypothesis is true, that is, P (results | H0).

Therefore, if we interpret that the probability that H0 is true in view of our results (P (H0 | results)) is equal to the value of p (P (results | H0)) we will be falling into an inverse fallacy or transposition of conditionals fallacy.

In fact, the probability that H0 is true does not depend only on the results of the study, but is also influenced by the previous probability that was estimated before the study, which is a measure of the subjective belief that reflects its plausibility, generally based on previous studies and knowledge. Let’s think we want to contrast an effect that we believe is very unlikely to be true. We will value with caution a p-value <0.05, even being significant. On the contrary, if we are convinced that the effect exists, will be settle for with little demands of p-value.

In summary, to calculate the probability that the effect is real we must calibrate the p-value with the value of the baseline probability of H0, which will be assigned by the researcher or by previously available data. There are mathematical methods to calculate this probability based on its baseline probability and the p-value, but the simplest way is to use a graphical tool, the Held’s nomogram, which you can see in the figure.

To use the Held’s nomogram we just have to draw a line from the previous H0 probability that we consider to the p-value and extend it to see what posterior probability value we reach. As an example, we have represented a study with a p-value = 0.03 in which we believe that the probability of H0 is 20% (we believe there is 80% that the effect is real). If we extend the line it will tell us that the minimum probability of H0 is 6%: there is a 94% probability that the effect is real. On the other hand, think of another study with the same p-value but in which we think that the probability of the effect is lower, for example, of 20% (the probability of H0 is 80%). For the same value of p, the minimum posterior probability of H0 is 50%, then there is 50% that the effect is real. As we can see, the posterior probability changes according to the previous probability.

And here we will end for today. We have seen how p-value only gives us an idea of the role that chance may have had in our results and that, in addition, may depend on other factors, perhaps the most important the sample size. The conclusion is that, in many cases, the p-value is a parameter that allows to assess in a very limited way the relevance of the results of a study. To do it better, it is preferable to resort to the use of confidence intervals, which will allow us to assess clinical relevance and statistical significance. But that is another story…

The fallacy of small p

A fallacy is an argument that appears valid but is not. Sometimes it’s used to deceive people and to give them a pig in a poke, but most of the time it is used for a much sadder reason: ignorance.

Today we will talk about one of these fallacies, very little known, but wherein we fall with great frequency when interpreting results of hypothesis testing.

Increasingly we see that scientific journals provide us with the exact value of p, so we tend to think that the lower the value of p the greater the plausibility of the observed effect.

To understand what we are going to explain, let us first remember the logic of falsification of the null hypothesis (H0). We start from a H0 that the effect does not exist, so we calculate the probability of finding such extreme results than those we found just by chance, given that H0 is true. This probability is the p-value, so that the smaller, the less likely that the result is due to chance and therefore most likely that the effect is real. The problem is that however small the p, there is always a probability of making a type I error and reject H0 being true (or what is the same, get a false positive and take for good an effect than does not really exist).

It is important to note that the p-value only indicates whether we have reached the threshold for statistical significance, which is a totally arbitrary value. If we get a threshold value of p = 0.05 we tend to think of the following four possibilities:

  1. That there is a 5% chance that the result is a false positive (that H0 is true).
  2. That there is a 95% chance that the effect is real (that H0 is false).
  3. The probability that the observed effect is due to chance is 5%.
  4. The rate of type I error is 5%.

However, all of this is wrong and we are falling in the inverse fallacy or the conditionals’ transposition fallacy. Everything is a problem of misunderstanding the conditional probabilities. Let’s see it slowly.

We are interested to know what the probability of H0 being true is given the results we have obtained. Expressed mathematically, we want to know P (H0 | results). However, the p-value is what gives us the probability of obtaining our results given that the null hypothesis is true, that is, P (result | H0).

Let’s see a simple example. The probability of being Spanish if you’re Andalusian is high (it should be 100%). The inverse is lower. The likelihood of having a headache if you have meningitis is high. The inverse is lower. If events are frequent, the probability will be higher than if they are rare. So, as we want to know P (H0 | results), we assess the baseline probability of H0 to avoid overestimating the evidence supporting that the effect is true.

If we think about it, it’s pretty intuitive. The probability of H0 before the study is a measure of a subjective belief that reflects their plausibility based on previous studies. Let’s think that we want to test an effect that we believe very unlikely to be true. We’ll assess with caution a p-value less than 0.05, albeit significant. On the contrary, if we are convinced that the effect exists, with little p we will be satisfied.

In short, to calculate the probability that the effect is real we will have to calibrate the p value with the value of the baseline probability of H0, which will be assigned by the investigator or by previous available data. Needless to say that there is a mathematical method to calculate the posterior probability of H0 according to their baseline probability and the p-value, but it would be rude to put a huge formula at this point of post.

held_enInstead, we will use a simpler method, using a graphic resource named Held’s nomogram that you can see in the figure.

To use the Held’s nomogram all we have to do is to draw a line from the previous probability that we think has H0 and prolong it through the p-value until reach the value of the posterior probability.

Imagine one study with a marginal value of p = 0.03 in which we believe the probability of H0 is 20% (we believe there is an 80% chance that the effect is real). If we draw the line we’ll get a minimum probability of H0 of 6%: there is a 94% chance that the effect is real.

On the other hand, think of another study with the same value of p but in which we think the probability of the effect is lower, for example, 20% (the probability of H0 is 80%). For the same value p, the subsequent minimum probability of H0 is 50%, and then there is a 50% chance that the effect is real. We see how the posterior probability changes with the previous probability.

And here we leave it. Surely this Held’s nomogram has reminded you of another much more famous nomogram but with a similar philosophy: the Fagan’s nomogram. This is used to calculate the post-test probability based on the pretest probability and the likelihood ratio of a diagnostic test. But that is another story…