I am not a fan of NHST (Null Hypothesis Significance Testing). Or maybe I should say, I am no longer a fan. I used to believe that rejecting null-hypotheses of zero differences based on the p-value was the proper way of gathering evidence for my substantive hypotheses. And the evidential nature of the p-value seemed so obvious to me, that I frequently got angry when encountering what I believed were incorrect p-values, reasoning that if the p-value is incorrect, so must be the evidence in support of the substantive hypothesis.

For this reason, I refused to use the significance tests that were most frequently used in my field, i.e. performing a by-subjects analysis and a by-item analysis and concluding the existence of an effect if both are significant, because the by-subjects analyses in particular regularly leads to p-values that are too low, which leads to believing you have evidence while you really don’t. And so I spent a huge amount of time, coming from almost no statistical background – I followed no more than a few introductory statistics courses – , mastering mixed model ANOVA and hierarchical linear modelling (up to a reasonable degree; i.e. being able to get p-values for several experimental designs). Because these techniques, so I believed, gave me correct p-values. At the moment, this all seems rather silly to me.

I still have some NHST unlearning to do. For example, I frequently catch myself looking at a 95% confidence interval to see whether zero is inside or outside the interval, and actually feeling happy when zero lies outside it (this happens when the result is statistically significant). Apparently, traces of NHST are strongly embedded in my thinking. I still have to tell myself not to be silly, so to say.

One reason for writing this blog is to sharpen my thinking about NHST and trying to figure out new and comprehensible ways of explaining to students and researchers why they should be vary careful in considering NHST as the sine qua non of research. Of course, if you really want to make your reasoning clear, one of the first things you should do is define the concepts you’re reasoning about. The purpose of this post is therefore to make clear what my “definition” of NHST is.

My view of NHST is very much based on how Gigerenzer et al. (1989) describe it:

“Fisher’s theory of significance testing, which was historically first, was merged with concepts from the Neyman-Pearson theory and taught as “statistics” *per se*. We call this compromise the “hybrid theory” of statistical inference, and it goes without saying the neither Fisher nor Neyman and Pearson would have looked with favor on this offspring of their forced marriage.” (p. 123, italics in original).

Actually, Fisher’s significance testing and Neyman-Pearson’s hypothesis testing are fundamentally incompatible (I will come back to this later), but almost no texts explaining statistics to psychologists “presented Neyman and Pearson’s theory as an alternative to Fisher’s, still less as a competing theory. The great mass of texts tried to fuse the controversial ideas into some *hybrid* statistical theory, as described in section 3.4. Of course, this meant doing the impossible.” (p. 219, italics in original).

So, NHST is an impossible, as in logically incoherent, “statistical theory”, because it (con)fuses concepts from incompatible statistical theories. If this is true, which I think it is, doing science with a small s, which involves logical thinking, disqualifies NHST as a main means of statistical inference. But let me write a little bit more about Fisher’s ideas and those of Neyman and Pearson, to explain the illogic of NHST.

I will try to describe the main characteristics of the two approaches that got hybridized in NHST at a conceptual level. I will have to simplify a lot and I hope these simplifications do little harm. Let’s start with Fisher’s significance testing.

### Fisher’s significance testing

The main purpose of Fisher’s significance testing is gathering evidence about parameters in a statistical model on the basis of a sample of data. So, the nature of the approach is

**evidential**. Crucially, the evidence the data provides can only be evidence against a statistical model, but it can not be evidence in favour of the model, much in line with Popper’s idea of progress in science by means of falsification. The statistical model to be nullified, i.e. the model one tries to obtain evidence against, is called the null-hypothesis.

Conceptually, the statistical model is a descriptive model of a population of possible values. An important part of Fisher’s approach is therefore to judge what kind of model provides an appropriate model of the population. For instance, this process of formulating the model (which, of course, involves a lot of thought and judgement) may lead one to assume that the random variable has a normal distribution, which is characterized by only two parameters, μ the expected value or mean of the distribution and σ, the square root of the variance of the distribution, which in the case of the normal distribution is it’s standard deviation (the standard deviation is the square root of the variance).

The values of μ and σ (or σ^{2}) are generally unknown, but we may assume (again as a result of thinking and judging) that they have particular values. For reasons of exposition, I will now assume that the value of σ is known, say σ = 15, so that we only have to take the unknown value of μ into account. Let’s suppose that our thinking and judging has led us to assume that the unknown value of μ = 100. The null-hypothesis is therefore that the variable has a normal distribution with μ = 100, and σ = 15.

We can obtain evidence against this null-hypothesis, by determining a p-value. We first gather data, say we take a random sample of N = 225 participants, which enables us to obtain observed values of the variable. Next, we calculate a test statistic, for example by estimating the value of μ (on the basis of our data) subtracting the hypothesized value and dividing the estimate by it’s standard error. Our estimated value may for example be 103, and the standard error equals 15 / √225 = 1.0, so the value of the test statistic equals (103 – 100) / 1 = 3. And now we are ready to calculate the p-value.

The p-value is the probability of obtaining (when sampling repeatedly) a value of the test statistic as large as or larger than the one obtained in the study, provided that the null-hypothesis is true. This probability can be calculated because the exact distribution of the test statistic can be deduced from the specification of the null-hypothesis. In our example, the test statistic is approximately normally distributed with μ = 0, and σ = 1.0. (The distribution is approximately normal, assuming the null-hypothesis is true, so the p-value in our example not exact). The p-value equals 0.003. (This is the so-called two-sided p-value, it is the probability of obtaining a value equal or larger than 3 or equal of smaller than -3, but we will ignore the technicalities of two-sided tests).

The p-value tells us that if the null-hypothesis is true, and we repeatedly take random samples from the population (as described by the null-hypothesis) we will find a value of our test statistic or a larger value in 0.3% of these samples. Thus, the probability of obtaining a value equal to or larger than 3.0 is very small.

Following Fisher, this low p-value can be interpreted as that something “improbable” occurred (assuming the null-hypothesis is true) or as inductive evidence against the null-hypothesis, i.e. the null-hypothesis is not true.

In his early writings Fisher proposed a p-value smaller than .05 as inductive evidence against the null-hypothesis (keeping in mind the possibility that the null is true, but that something improbable happened), but later he thought using the fixed criterion of .05 to be non-scientific. If the p-value is smaller than the criterion (say .05), the result is statistically significant.

In sum, the approach by Fisher, significance testing, involves specifying a statistical model, and using the p-value to test the assumptions of the model, such as specific values for μ or σ. If the p-value is smaller than the criterion value, either something improbable occurred or the null-hypothesis is not true. Crucially, the p-value may provide inductive evidence against the assumptions of the null-hypothesis, but a large p-value (larger than the criterion value) is not inductive support for the null-hypothesis.

###

### Neyman-Pearson hypothesis testing

In contrast to Fisher’s evidential approach, Neyman and Pearson’s hypothesis testing is **non-evidential**. Its primary goal is to choose on the basis of repeated random sampling between two hypotheses (or more; but I will only consider two) in order to make behavioral decisions (so to speak) that will minimize decision errors and their associated costs (loss) in the long run. In stead of trying to figure out which of the two hypotheses is true, one decides to accept one (and reject the other) of the two hypothesis *as if it were true, without actually having to believe it*, and act accordingly.

As with Fisher, Neyman-Pearson hypothesis testing starts with formulating descriptive models of the population. We may for instance propose (after thinking and judging) that one model (hypothesis H

_{1}) assumes that the variable has a normal distribution with μ = 100 and one model (hypothesis H

_{2}) that assumes that the variable has a normal distribution with μ = 106. We will assume the value of σ is known, say it equals 15. We will have to choose one of the two hypothesis, by rejecting one (and accepting the other).

Let’s suppose that __only one of the models is true and that they cannot both be false.__ This means that we can incorrectly decide to reject or accept each of the two hypotheses. That is, if we incorrectly reject H_{1}, we incorrectly accept H_{2}. So, there are two types of errors we can make. A type I error occurs when we incorrectly reject a true hypothesis and a type II error occurs when we incorrectly accept a false hypothesis.

In a previous post (

here), I used the following conceptual descriptions of these errors: the type I error is the error of excessive skepticism, and the type II error is the error of extreme gullibility, but from the perspective of Neyman-Pearson hypothesis testing these conceptual descriptions may not make much sense, because these terms imply a relation between the decisions about a hypothesis and belief in the hypothesis, while in the Neyman-Pearson approach a rejection or non-rejection does not lead to commitment in believing or not believing the hypothesis, although the hypotheses themselves are based on beliefs (and judging and reasoning) that the descriptive model is suitable for the population at hand.

The crucial point is that the goal of Neyman-Pearson hypothesis testing is to base courses of action on the decision to reject or not-reject a statistical hypothesis. This entails minimizing the costs (loss) associated with type I and type II errors. In particular, the approach minimizes the probability (β) of a type II error bounded by the probability (α) of a type I error. We may also say that we want to maximize the probability (1 – β), the probability of rejecting a false hypothesis, the so called power of the test, while keeping α at a maximum (usually low) value.

Suppose, that our considerations of the loss associated with type I and type II errors, has led us to the insight that false rejection of H

_{2} is the most costly error. And suppose that we have agreed/determined/reasoned/judged that the probability of falsely rejecting it should be at most .05. So, α = .05. Of course, we also “know” the loss associated with falsely accepting it, and we have determined that the probability β should not exceed .10. Now, suppose that we repeatedly sample N = 225 observations from the (unknown) population. We do not know whether H

_{1} or H

_{2} provides the correct description of the population, but we assume that one of them must be true if we select a particular sample, and they cannot both be false.

We will reject H_{2} (Normal distribution with μ = 106, and σ = 15) if the sample mean in our random sample equals 104.35 or less (this corresponds to a test statistic with value -1.65). Why, because the probability of obtaining a sample mean equal or smaller than 104.35 is approximately .05 when H_{2} is true. Thus, if we repeatedly sample from the population when H_{2} is true, we will incorrectly reject it in 5% of the cases. Which is the probability of a type I error that we want.

We have arranged things so, that when H

_{2} is false, H

_{1} is per definition true. If H

_{1} is true (H

_{2} is false), there is a probability of approximately .99 to obtain a sample mean of 104.35 or smaller. Thus, the probability to reject H

_{2} when it it false is .99, this is the power of the test, and the probability is approximately .01 of incorrectly not rejecting H

_{2} when it is false. The latter probability is the probability of a type II error, which we did not want to be larger than .10.

Now suppose the results is that the sample mean equals 103 (the value of the test statistic equals -3). According to the decision criterion we reject H_{2} (with α = .05) and accept H_{1} and act as if μ = 100 is true. Crucially, we do not have to believe it is actually true, nor do we consider the test statistic with value -3 as inductive evidence against H_{2}. So, the test result provides neither support for H_{1} nor evidence against H_{2}, but we know from the specification of the models and the assumptions about sampling that repeatedly using this procedure leads to 5% type I errors and 1% type II errors in the long run, depending on which of the two hypotheses is true (which is unknown to us). Given that we know the loss associated with each error, we are able to minimize the expected loss associated with acting upon the decisions we make about the hypotheses.

Note that Fisher’s significance testing would consider the p-value associated with the test statistic of -3, i.e. p < .01 either as inductive evidence against H_{2} or as an indication that something unusual (improbable) happened assuming H_{2} is true. Note also that in Fisher’s approach, it is not possible to reason from the inferred untruth of H_{2} to the truth of H_{1}, because H_{1} does not exist in that approach.

It should be noted further that in the Neyman-Pearson approach, the importance of the value of the test statistic is restricted to whether or not the value exceeds a critical value (i.e. whether or not the value of the statistic is in the rejection region). That means that it is of no concern how much the test statistic exceeds the critical value, since all values larger than the critical value lead to the same decision: reject the hypothesis. In other words, because the approach is non-evidential, the magnitude of the test statistic is inconsequential as far as the truth of the hypothesis is concerned. Compare this to the Fisher approach, where the larger the test statistic is (the smaller the p-value), the stronger the inductive evidence is against the null-hypothesis.

### Null-hypothesis significance testing (NHST)

NHST combines Fisher’s significance testing with Neyman-Pearson hypothesis testing, without regard for the logical incompatibilities of the two approaches. Fisher’s p-value is used both as a measure of inductive evidence against the null-hypothesis, with smaller p-values considered to be stronger evidence against the null than larger p-values, and as a test statistic. In its latter use, the null-hypothesis is (usually) rejected if the p-value is smaller than .05.

Contrary to significance testing, NHST uses the p-value to decide between the null-hypothesis and an alternative hypothesis. But contrary to the Neyman-Pearson approach, α, the probability of a type I error is not based on judgement and careful consideration of loss-functions, but is mechanically set at .05 (or .01). And, contrary to the Neyman-Pearson approach, the probability of a type II error (β) is usually not considered.

One reason for the latter may be that specification of the null-hypothesis is also mechanized. In the case of differences between means or testing correlations or regression coefficients, etc, the standard null-hypothesis is that the difference, the correlation or the coefficient equals 0. This is also called the nil-hypothesis. As the alternative excludes the null, the standard alternative hypothesis is that the parameter in question is not equal to zero, which makes it hard to say something about the type II error, because determining the probability of a type II error requires thinking about a minimal consequential effect size (consequential in terms of decisions and associated loss) that can serve as the alternative hypothesis.

Specifying a non-nil alternative hypothesis, i.e. that the parameter value is not equal to zero, implies that results arbitrarily close to nil, but not equal to nil, are as consequential as effect sizes that are far away from the null-value, both in acting upon the value as in not-acting upon it. Crucially, not specifying a minimal consequential effect size, rules out determining β. So, even though NHST uses the concept of an alternative hypothesis (contrary to Fisher), the nil-hypothesis is such that the procedure of Neyman and Pearson can no longer work: it is impossible to strike a balance between loss associated with type I and type II errors, and so NHST is not a hypothesis testing procedure.

For these reasons I am very much inclined to characterize NHST as fixed-α significance testing. But using fixed-α in combination with an evidential interpretation of p-values leads to logical inconsistencies. (As always, I assume that being logically consistent is one of the characteristics of doing science, but maybe you disagree). Note, by the way, that I am talking about the p-value as measure of evidence against the nil-hypothesis, and not about the p-value as test statistic. (But remember that proper use of the p-value as test statistic requires being able to specify a non-nil alternative hypothesis).

One of the logical inconsistencies is that α and the p-value-as-evidence involve contradictory conceptualisations of probability. In terms of p-values, α is simply the probability that the p-value is smaller than .05 (the usual criterion) assuming the nil-hypothesis is true. That probability follows deductively from the specification of the null-hypothesis (including, of course, the statistical model underlying it). Note that α is completely independent of actually realized results: it an assertion about the p-value assuming repeated sampling from the null-population; α is about the test-procedure and not about actual data.

But the p-value-as-evidence against the null is not the result of deductive reasoning, but of inductive reasoning. The p-value is not a probability associated with the test-procedure. It is a random variable the value of which depends on the actual data, the null-value and the statistical model. Crucially, from a single realized result (a p-value) an inference is made about a probability distribution. But this is inconsistent with the frequency interpretation of probability that underlies the conceptualisation of α, because under this interpretation no probability statement can be made about realized single results (except that the probability is 100% that it happened) or about an unrealized single result (that probability is 0 if it does not happen or 1.0 if it happens). To make the point: using p-value-as-evidence and (fixed)-α requires both believing that probability statements can be made on the basis of a single result and believing that that is impossible. So, it boils down to believing that both A and not-A are true.

To me, logical inconsistencies like these disqualify NHST as a scientific means of statistical inference. I repeat that this is because I believe that doing science entails being logically consistent. Assuming or believing that A and not-A are both true, is not an example of logical consistency.