Sample size planning for precision: the basics

In this post, I will introduce some of the ideas underlying sample size planning for precision. The ideas are illustrated with a shiny-application which can be found here: The app illustrates the basic theory considering sample size planning for two independent groups. (If the app is no longer available (my allotted active monthly hours are limited on, contact me and I’ll send you the code).

The basic idea

The basic idea is that we are planning an experiment to estimate the difference in population means of an experimental and a control group. We want to know how many observations per group we have to make in order to estimate the difference between the means with a given target precision. 
Our measure of precision is the Margin of Error (MOE).  In the app, we specify our target MOE as a fraction (f) of the population standard deviation. However, we do not only specify our target MOE, but also our desired level of assurance. The assurance is the probability that our obtained MOE will not exceed our target MOE. Thus, if the assurance is .80 and our target MOE is f = .50, we have a probability of 80% that our obtained MOE will not exceed f = .50. 
The only part of the app you need for sample size planning is the “Sample size planning”-form. Specify f, and the assurance, and the app will give you the desired sample size. 
If you do that with the default values f = .50 and Assurance  = .80, the app will give you the following results on the Planning Results-tab:  Sample Size: 36.2175, Expected MOE (f): 0.46. This tells you that you need to sample 37 participants (for instance) per group and then the Expected MOE (the MOE you will get on average) will equal 0.46 (or even a little less, since you sample more than 36.2175 participants). 
The Planning-Results-tab also gives you a figure for the power of the t-test, testing the NHST nil-hypothesis for the effect size (Cohen’s d) specified in the “Set population values”-form. Note that this form, like the rest of the app provides details that are not necessary for sample size planning for precision, but make the theoretical concepts clear. So, let’s turn to those details. 

The population

Even though it is not at all necessary to specify the population values in detail, considering the population helps to realize the following. The sample size calculations and the figures for expected MOE and power, are based on the assumption that we are dealing with random samples from normal populations with equal variances (standard deviations). 
From these three assumptions, all the results follow deductively.  The following is important to realize:  if these assumptions do not obtain, the truth of the (statistical) conclusions we derive by deduction is no longer guaranteed. (Maybe you have never before realized that sample size planning involves deductive reasoning; deductive reasoning is also required for the calculation of p-values and to prove that 95% confidence intervals contain the value of the population parameter in 95% of the cases; without these assumptions is it uncertain what the true p-value is and whether or not the 95% confidence interval is in fact a 95% confidence interval).

In general, then, you should try to show (to others, if not to yourself) that it is reasonable to assume normally distributed populations, with equal variances and random sampling, before you decide that the p-value of your t-test, the width of your confidence interval, and the results of sample size calculations are believable.

The populations in the app are normal distributions. By default, the app shows two such distributions. One of the distributions, the one I like to think about as corresponding to the control condition, has μ = 0, the other one has μ = 0.5. Both distributions have a standard deviation (σ = 1). The standardized difference between the means is therefore equal to δ = 0.50.

The default populations are presented in Figure 1 below.

normal populations
Figure 1: Two normal distributions. The distribution to the left has μ = 0, the one to the right has μ = 0.5 The standard deviation in both distributions equals σ = 1. The standardized difference δ and the unstandardized difference between the means both equal 0.50. 

The sampling distribution of the mean difference 

The other default setting in the app is a sample size (per group) of n = 20.  From the sample size and the specification of the populations, we can deduce the probability density of the different values of the estimates of the difference between the population means. The estimate is simply the difference between the sample means.

This so-called sampling distribution of the mean difference is depicted on the tab next to the population. Figure 2 shows what the sampling distribution looks like if we repeatedly draw random samples of size n = 20 per group from our populations and keep track of the difference between the sample means we get in each repetition.

sampling distribution of difference
Figure 2: Sampling distribution of the difference between two sample means based on samples of n = 20 per group and random sampling from the populations described in Figure 1. 

Note that the mean of the sampling distribution equals 0.5 (as indicated by the middle vertical line). This is of course the (default) difference between the population means in the app. So, on average, estimates of the population difference equal the population difference.

The lines to the left and the right of the mean indicate the mean plus or minus the Margin of Error (MOE). The values corresponding to the lines are 0.5 ± MOE. 95% of estimates of the population mean difference have a value between these lines.

Conceptually, the purpose of planning for precision is to decrease the (horizontal) distance between these lines and the population mean difference. In other words, we would like the left and right lines as close to the mean of the distribution as is practically acceptable and possible.

The distribution of the t-statistic 

The tab next to the sampling distribution tab contains a figure representing the sampling distribution of the t-statistic. The sampling distribution of t can be deduced on the basis of the population values and the sample size.  In the app, it is assumed that t is calculated under the assumption that the null-hypothesis of zero difference between the means is true. The sampling distribution of t is what you get if you repeatedly sample from the populations as specified, calculate the t-statistic and keep a record of the values of the t-statistic.

The sampling distribution of the t-statistic presented in Figure 3 contains two vertical lines. These lines are located (horizontally) on the value of t that would lead to rejection of the null-hypothesis of equal population means. In other words, the lines are located at the critical value of t (for a two-tailed test).

distribution of t
Figure 3: Distribution of the t-statistic testing the null-hypothesis of equal population means. The distribution is based on sampling from the populations described in Figure 3. The sample size is n = 20 per group. The lines represent the critical value of t for a two sided t-test. The area between the vertical lines is the probability of a type II error. The combined areas to the left of the left line and to the right of the right line is the power of the test. 

The area between the lines is the probability that the null-hypothesis will not be rejected. In the case of a true population mean difference (which is the default assumption in the app), that probability is the probability of an error of the second kind: a type II error.

The complement of that probability is called the power of the test. This is, of course, the area to the left of the left vertical line added to the area to the right of the right vertical line. Conceptually, the power of the test is the probability of rejecting the null-hypothesis when in fact it is false.

Figure 3 clearly demonstrates that if the true mean difference equals 0.50 and the sample size (per group) equals n = 20, that there is a large probability that the null-hypothesis will not be rejected. Actually, the probability of a type II error equals .66. (So, the power of the test is .34).

Sample size planning for precision

With respect to sample size planning for precision, the app by default takes half of a standard deviation (f = .50) as the target MOE. Besides, planning is with 80% assurance. This means that the default settings search for a sample size (per group), so that with 80%  probability MOE will not exceed 0.50 (Note that the default value of the standard deviation is 1, so an f of .50 corresponds to a target MOE of  0.50 on the scale of the data; Likewise, were the standard deviation equal to 2, an f of .50 would correspond to a target MOE of 1.0).

As described above, planning with the default values gives us a sample size of  n = 37 per group, with an expected MOE of 0.46. In the tab next to the planning results, a figure displays what you can expect to find on average, given the planned sample size and the specification of the population. That figure is repeated here as Figure 4.

Expected results
Figure 4: Expected results in terms of point and interval estimates (95% confidence intervals). This is what you will find on average given the population specification in Figure 1 and using the default values for sample size planning. 

Figure 4 displays point and interval estimates of the group means and the difference between the means. The interval estimates are 95% confidence intervals. The figure clearly shows that on average, our estimate of the difference is very imprecise. That is, the expected 95% confidence interval ranges from almost 0 (0.50 – 0.46 = 0.04) to almost 1 (0.50 + 0.46 = 0.96). Of course, using n = 20, would be worse still.

A nice thing about the app (well, I for one think it’s pretty cool) is that as soon as you ask for the sample sizes, the sample size in the set population values form is automatically updated. Most importantly, this will also update the sampling distribution graphs of the difference between the means and the t-statistic. So, it provides an excellent way of showing what the updated sample size means in terms of MOE and the power of the t-test.

Let’s have a look at the sampling distribution of the mean difference, see Figure 5.

Sampling distribution of the difference.
Figure 5: Sampling distribution of the mean difference with n = 37 per group. Compare with Figure 2 to see the (small) difference in the Margin of Error compared to n = 20.  

If you compare Figures 5 and 2, you see that the vertical lines corresponding to the mean plus and minus MOE have shifted somewhat towards the mean. So here you can see, that almost doubling the sample size (from 20 to 37) had the desired effect of making MOE smaller.

I would like to point out the similarity between the sampling distribution of the difference and the expected results plot in Figure 4. If you look at the expected results for our estimate of the population difference, you see that the point estimate corresponds to the mean of the sampling distribution, which is of course equal to the populations mean difference and that the limits of the expected confidence interval correspond to the left and right vertical lines in Figure 5. Thus, on average the limits of the confidence interval correspond to the values that mark the middle 95% of the sampling distribution of the samples mean difference.

Since we specified an assurance of 80%, there is an 80% probability that in repeated sampling from the populations (see Figure 1) with n = 37 per group, our (estimated) MOE will not exceed half a standard deviation. Thus, whatever the true value of the populations mean difference is, there is a high probability that our estimate will not be more than half a standard deviation away from the mean. This is, I think, one of the major advantages of sample size planning for precision: we do not have to specify the unknown population mean difference. This is in contrast to sample size planning for power, where we do have to specify a specific population mean difference.

Speaking of power, the results of the sample size planning suggest that for our specification of the populations mean difference (Cohen’s delta = 0.50) the power of the test equals 0.56. Thus, there is a probability of 56% that with n = 37 per group the t-test will reject. The probability of a type II error is therefore 44%.

Figure 6 shows the distribution of the t statistic with n = 37 per group and a standardized effect size of 0.50.

Distribution of the t statistic
Figure 6. The distribution of the t-statistic testing the null-hypothesis of equal population means. The distribution is based on the population specification in Figure 1 and sample sizes of n = 37 per group, with true effect size equal to 0.50. The probability of a type II error is the area of under the curve between the two vertical lines. The power is the area under the curve beyond the two lines. Compare with Figure 3 to see the differences in these probabilities compared to n = 20.

Power versus precision

Now suppose that the unstandardized mean difference between the population means equals 2 and that the standard deviation equals 2.5.  I just filled in the set population values form, setting the mean of population 2 to 2.0 and the standard deviation to 2.5. And I clicked set values.

Let us plan for a target MOE of  f = 0.5 standard deviations with 80% assurance. Click get sample sizes in the sample size planning form. In this case, target MOE equals 1.25.

The results are not very surprising. Since the f did not change compared to the previous time, the results as regards the sample size are exactly the same. We  need n = 37. Again, this is what I like about sample size planning, no matter what the unknown situation in the population is, I just want my margin of error to be no more than half a standard deviation (for example).

But the power did change (of course). Since the standardized population mean difference is now 0.80 (= 2.0 / 2.5) in stead of 0.50, and all the other specifications remained the same, the power increases from 56% to 92%. That’s great.

However, the high probability of rejecting the null-hypothesis does not mean that we get precise estimates. On average, the point estimate of the difference equals 2 and the 95% confidence limits are  0.85 and 3.15 (the point estimate plus or minus 0.46 times the standard deviation of 2.5). See Figure 7.

Expected results large standardized effect
Figure 7: Expected results using n = 37 when sampling from two normal populations with equal standard deviations (σ = 2.5) and mean difference of 2.0. The standardized effect size equals 0.80. Note the imprecision of the estimates even though the power of the t-test equals .92.

In short, even though there is a high probability of  (correctly) rejecting the null-hypothesis of equal population means, we are still not in the position to confidently conclude what the size of the difference is: the expected confidence interval is very wide. 

The omnibus F-test may be ignored if you use multiple comparison procedures

I think  trying to be scientific with a small s involves asking critical questions about  common wisdom or common practice. In this post, I would like to focus on multiple comparisons in the context of ANOVA. What does common practice indicate?

Common wisdom suggests doing multiple comparisons only if the F-test is significant

Let’s have a look on some practical advice considering multiple comparisons found on the web ( and in Field (2015).

“One way to begin an ANOVA is to run a general omnibus test. The advantage to starting here is that if the omnibus test comes up insignificant, you can stop your analysis and deem all pairwise comparisons insignificant. If the omnibus test is significant, you should continue with pairwise comparisons” (

“When we have a statistically significant effect in ANOVA and an independent variable of more than two levels, we typically want to make follow-up comparisons. There are numerous methods for making pairwise comparisons and this tutorial will demonstrate how to execute several different techniques in R.” (
And have a look at how the text book I used to use in my statistics course explains it.

“It might seem a bit unhelpful that an ANOVA doesn’t tell you which groups are different from which, given that having gone to the trouble of running an experiment, you probably need to know more than ‘there’s some difference somewhere or other’. You might wonder, therefore, why we don’t just carry out a lot of t-tests, which would tell us very specifically whether pairs of group means differ. Actually, the reason has already been explained in Section every time you run multiple tests on the same data you inflate the potential Type I errors that you make. However, we’ll return to this point in Section 11.5 when we look at how we follow up an ANOVA to discover where the group difference lie.” (Field, 2015, p. 442).
Although, in honesty, on p. 459 Field writes:

“The least significance difference (LSD) pairwise comparison makes no attempt to control Type I error and is equivalent to performing multiple t-tests on the data. The only difference is that LSD requires the overall ANOVA to be significant.”

This is meant to inform about the relative merits of one post hoc procedure to another in terms of Type I and Type II error.  Crucially, it is not mentioned that the other post-hoc procedures require that the overall ANOVA be significant. (As common wisdom seems to suggest). However,  his flow-chart of the ANOVA procedure (p. 460) clearly suggests multiple comparison procedures should be used as post-hoc procedures (after the ANOVA is significant).

Thus, common “statistical” wisdom seem to suggest that multiple comparison procedures are to be used as post hoc procedures following up a significant omnibus F-test. And the reason is that this two-stepped procedure minimizes the probability of type I errors.

Now, let’s ask ourselves whether this common sense is, well, sensible.

Multiple comparisons only after significant F-test affects power negatively

Wilcox (2017) contains some useful information regarding our question. In his discussion of the much used Tukey-HSD procedure (the Tukey-Kramer Method), he references Bernhardson, (1975) who shows that the probability of at least 1 type I error among pairwise comparisons of estimates of equal population means (i.e. true null-hypotheses) is no longer equal to \alpha if the procedure is only carried out following a significant omnibus test. That is, if we use our beloved two step procedure.

The consequence of the two step procedure for the Tukey-HSD is that \alpha is reduced. Thus, if we want our multiple comparisons procedure to generate one type I error or more at most with a probability of  \alpha = .05, using the 2 step procedure leads to a lowered \alpha. This is of course, bad news, because in the event that not all of the null-hypotheses are true, lowering \alpha increases \beta, the probability of not rejecting when the null-hypothesis is false (keeping the sample size constant, of course). In other words, the two step procedure decreases the power of the multiple comparison procedure.

In the words of Wilcox (2017):

“In practical terms, when it comes to controlling the probability of at least one type I error, there is no need to first reject with the ANOVA F test to justify using the Tukey-Kramer method. If the Tukey-Kramer method is used only after the F test rejects, power can be reduced. Currently, however, common practice is to use the Tukey-Kramer method only if the F-test rejects. That is, the insight reported by Bernhardson is not yet well known.”  (p. 385).

In conceptual terms,  the fact that the probability of at least one type I error in the multiple comparison procedure is  smaller than \alpha if the F-test rejects is pretty clear, at least to me it is. Suppose we reject if the p-value of the F-test is smaller or equal to 5%. This will also be the probability that we conduct the multiple comparison test over repeated replications of the same experiment. Of that 5%, not every application of the procedure will result in at least one type I error. Indeed, a puzzling fact for many beginning researchers is that the F-test is significant while none of the pairwise comparisons is. In other words, some of those 5%  of the cases in which we perform the procedure following a significant F-test will probably not reject any of the pairwise null-hypotheses, unless it is guaranteed that at least one type I error per application will be made.

(With no adjustment of \alpha for multiple comparisons, this will happen (with high probability so no guarantee) if a huge number of pairwise comparisons are made. For instance, with 99 unadjusted multiple comparisons the probability of at least one type I error is 99%.; this is why it makes sense to demand that the F-test is significant before testing multiple comparisons with the LSD procedure. Although the latter seems to run into trouble with more than 3 groups (Wilcox, 2017).

A quick simulation study

My hunch is that the two-step procedure is unnecessary for the Tukey-Kramer method as well as for other multiple comparison procedures (the exception Fisher’s LSD procedure which was designed as a post hoc procedure to be used as a follow up after a significant F-test, as Field (2015) rightly points out), but I only focused on the Tukey-Kramer method. What I did was a simple simulation study with a four group between subjects design (all \mu‘s equal) and estimated the probability of at least type I error both with and without using the 2 step procedure.

#number of groups
ngr = 4

#number of participants
n = 40

#group is a factor
gr <- factor(rep(1:ngr, each=n))

#vector for storing rejections F-test
Reject <- rep(0, 10000)

#vector for storing #rejections multiple
RejectHSD <- rep(0, 10000)

for (i in 1:10000) {

y = rnorm(ngr*n)
mod = aov(y ~ gr)
Reject[i] = anova(mod)$"Pr(>F)"[1] <= .05
PS <- TukeyHSD(mod)$gr[,4]
RejectHSD[i] = sum(PS <=.05)

#probability type I error F-test
## [1] 0.0515
#probability at least one type I error Tukey HSD
sum(RejectHSD > 0) / length(RejectHSD)
## [1] 0.0503
#probability at least one type I error given F-tests Rejects
sum(RejectHSD[Reject==TRUE] > 0) / length(RejectHSD)
## [1] 0.0424

Even though a single (relatively tiny) simulation (which, by the way, takes a long time to run, nonetheless), is not necessarily convincing, it does  illustrate the main points of this post. First, the probability of at least one incorrect rejection using the TukeyHSD function is close to .05. With this particular random seed it even performs a little better than the ANOVA F-test: .0503 versus .0515. This illustrates that even without considering whether the omnibus test is significant the main demand of not rejecting too many true null-hypotheses is completely satisfied. So, in practical terms, you can safely ignore the omnibus test if your concerns are about  \alpha.

Second, the probability of incorrectly rejecting at least one true pair-wise null-hypothesis after the ANOVA F-test is significant is estimated to be .0424. This shows, that the two-step procedure leads to a larger decrease in the actual type I error probability than is wanted. Even though this may seem good news from the perspective of avoiding type I errors, the down side is that pair wise null-hypotheses that are false (and potentially important) may not be detected.


Common wisdom and practice suggest that multiple comparisons procedures should be done only after a significant omnibus test. We have seen that this is not at all necessary if we use a multiple comparisons procedure that is designed to control the type I error probability. To my knowledge, most of the procedures conventionally thought of as post hoc tests are designed in this manner, the exception being the LSD procedure which does require a significant F-test. For practical purposes, then, do not bother with the omnibus test (note the exception) if you are planning to pair wise compare all the treatment means.
This practical advice does not mean, of course, that I am suggesting you spend your time comparing all treatment means. Most of the time, focused comparisons are a more fruitful way of analysing your data. But I’ll leave that topic for another time.


Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics. 4th Edition. London: Sage.
Wilcox, R. (2017). Understanding and Applying Basic Statistical Methods Using R. Hoboken, NJ: Wiley,

What is NHST, anyway?

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 σ2) 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 H1) assumes that the variable has a normal distribution with μ = 100 and one model (hypothesis H2) 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 H1, we incorrectly accept H2. 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  H2 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 H1 or H2 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 H2 (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 H2 is true. Thus, if we repeatedly sample from the population when H2 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 H2 is false, H1 is per definition true. If H1 is true (H2 is false), there is a probability of approximately .99 to obtain a sample mean of 104.35 or smaller. Thus, the probability to reject H2 when it it false is .99, this is the power of the test, and the probability is approximately .01 of incorrectly not rejecting H2 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 H2 (with α = .05) and accept H1 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 H2. So, the test result provides neither support for H1 nor evidence against H2, 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 H2 or as an indication that something unusual (improbable) happened assuming H2 is true. Note also that in Fisher’s approach, it is not possible to reason from the inferred untruth of H2 to the truth of H1, because H1 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.

Type I error probability does not destroy the evidence in your data

 Have you heard about that experimental psychologist? He decided that his participants did not exist, because the probability of selecting them, assuming they exist, was very small indeed (p < .001). Fortunately, his colleagues were quick to reply that he was mistaken. He should decide that they do exist, because the probability of selecting them, assuming they do not exist, is very remote (p < .001). Yes, even unfunny jokes can be telling about the silliness of significance testing.

But sometimes the silliness is more subtle, for instance in a recent blog post by Daniel Lakens, the 20% Statistician with the title “Why Type I errors are more important than Type 2 errors (if you care about evidence).” The logic of his post is so confused, that I really do not know where to begin. So, I will aim at his main conclusion that type I error inflation quickly destroys the evidence in your data.

(Note: this post uses mathjax and I’ve found out that this does not really work well on a (well, my) mobile device. It’s pretty much unreadable).

Lakens seems to believe that the long term error probabilities associated with decision procedures, has something to do with the actual evidence in your data. What he basically does is define evidence as the ratio of power to size (i.e. the probability of a type I error), it’s basically a form of the positive likelihood ratio

    \[PLR = \frac{1 - \beta}{\alpha},\]

which makes it plainly obvious that manipulating \alpha (for instance by multiplying it with some constant c) influences the PLR more than manipulating \beta by the same amount.  So, his definition of  “evidence” makes part of his conclusion true, by definition:  \alpha has more influence on the PLR than \beta,  But it is silly to reason on the basis of this that the type I error rate destroys the evidence in your data.

The point is that \alpha  and \beta (or the probabilities of type I errors and type II errors) have nothing to say about the actual evidence in your data. To be sure, if you commit one of these errors, it is the data (in NHST combined with arbitrary i,e, unjustified cut-offs) that lead you to these errors. Thus, even \alpha = .01 and \beta = .01, do not guarantee that actual data lead to a correct decision.

Part of the problem is that Lakens confuses evidence and decisions, which is a very common confusion in NHST practice. But, deciding to reject a null-hypothesis, is not the same as having evidence against it (there is this thing called type I error). It seems that NHST-ers and NHST apologists find this very very hard to understand. As my grandmother used to say: deciding that something is true, does not make it true

I will try to make plausible that decisions are not evidence (see also my previous post here). This should be enough to show you that error probabilities associated with the decision procedure tells you nothing about the actual evidence in your data. In other words, this should be enough to convince you that Type 1 error rate inflation does not destroy the evidence in your data, contrary to the 20% Statistician’s conclusion.

Let us consider whether the frequency of correct (or false) decisions is related to the evidence in the data. Suppose I tell you that I have a Baloney Detection Kit (based for example on the baloney detection kit at and suppose I tell you that according to my Baloney Detection Kit the 20% Statistician’s post is, well, Baloney. Indeed, the quantitative measure (amount of Baloneyness) I use to make the decision is well above the critical value. I am pretty confident about my decision to categorize the post as Baloney as well, because my decision procedure rarely leads to incorrect decisions. The probability that I decide that something is Baloney when it is not is only \alpha = .01 and the probability that I decide that something is not-Baloney when it is in fact Baloney is only 1% as well (\beta = .01).

Now, the 20% Statistician’s conclusion states that manipulating \alpha, for instance by setting \alpha = .10 destroys the evidence in my data. Let’s see. The evidence in my data is of course the amount of Baloneyness of the post. (Suppose my evidence is that the post contains 8 dubious claims). How does setting \alpha have any influence on the amount of Baloneyness? The only thing setting \alpha does is influence the frequency of incorrect decisions to call something Baloney when it is not. No matter what value of \alpha (or \beta, for that matter) we use, the amount of Baloneyness in this particular post (i.e. the evidence in the data) is 8 dubious claims.

To be sure, if you tell the 20% Statistician that his post is Baloney, he will almost certainly not ask you how many times you are right and wrong on the long run (characteristics of the decision procedure), he will want to see your evidence. Likewise, he will probably not argue that your decision procedure is inadequate for the task at hand (maybe it is applicable to science only and not to non-scientific blog posts), but he will argue about the evidence (maybe by simply deciding (!) that what you are saying is wrong; or by claiming that the post does not contain 8 dubious claims, but only 7).

The point is, of course, this: the long term error probabilities \alpha and \beta associated with the decision procedure, have no influence on the actual evidence in your data.  The conclusion of the 20% Statistician is simply wrong. Type I error inflation does not destroy the evidence in your data, nor does type II error inflation.

Decisions are not evidence

The thinking that lead to this post began with trying to write something about what Kline (2013) calls the filter myth. The filter myth is the arguably – in the sense that it depends on who you ask – mistaken belief in NHST practice that the p-value discriminates between effects that are due to chance (null-hypothesis not rejected) and those that are real (null-hypothesis rejected). The question is whether decisions to reject or not reject can serve as evidence for the existence of an effect.

Reading about the filter myth made me wonder whether NHST can be viewed as a screening test (diagnostic test), much like those used in medical practice. The basic idea is that if the screening test for a particular condition gives a positive result, follow-up medical research will be undertaken to figure out whether that condition is actually present. (We can immediately see, by the way, that this metaphor does not really apply to NHST, because the presumed detection of the effect is almost never followed up by trying to figure out whether the effect actually exists, but the detection itself is, unlike the screening test, taken as evidence that the effect really exists; this is simply the filter myth in action).

Let’s focus on two properties of screening tests. The first property is the Positive Likelihood Ratio (PLR). The PLR is the ratio of the probability of a correct detection to the probability of a false alarm. In NHST-as-screening-test, the PLR  equals the ratio of the power of the test to the probability of a type I error: PLR = (1 – β) / α. A high value of the PLR means, basically, that a rejection is more likely to be a rejection of a false null than a rejection of a true null, thus the PLR means that a rejection is more likely to be correct than incorrect.

As an example, if β = .20, and α = . 05, the PLR equals 16. This means that a rejection is 16 times more likely to be correct (the null is false) than incorrect (the null is true).

The second property I focused on is the Negative Likelihood Ratio (NLR). The NLR is the ratio of the frequency of incorrect non-detections to the frequency of correct non-detections. In NHST-as-screening-test, the NLR equals the ratio of the probability of a type II error to the probability of a correct non-rejection: NLR = β / (1 – α). A small value of the NLR means, in essence, that a non-rejection is less likely to occur when the null-hypothesis is false than when it is true.

As an example, if β = .20, and α = . 05, the NLR equals .21. This means that a non-rejection is .21 times more likely (or 4.76 (= 1/.21) times less likely) to occur when the null-hypothesis is false, than when it is true.

The PLR and the NLR can be used to calculate the likelihood ratio of the alternative hypothesis to the null-hypothesis given that you have rejected or given that you have not-rejected, the posterior odds of the alternative to the null. All you need is the likelihood ratio of the alternative to the null before you have made a decision and you multiply this by the PLR after you have rejected, and by the NLR after you have not rejected.

Suppose that we repeatedly (a huge number of times) take a random sample from a population of null-hypotheses in which 60% of them are false and 40% true. If we furthermore assume that a false null means that the alternative must be true, so that the null and the alternative cannot both be false, the prior likelihood of the alternative to the null equals p(H1)/p(H0) = .60/.40 = 1.5. Thus, of all the randomly selected null-hypotheses, the proportion of them that are false is 1.5 times larger than the proportion of  null-hypotheses that are true. Let’s also repeatedly sample (a huge number of times) from the population of decisions. Setting β = .20, and α = . 05, the proportion of rejections equals p(H1)*(1 – β) + p(H0)*α = .60*.80 + .40*.05 = .50 and the proportion of non-rejections equals p(H1)*β + p(H0)*(1 – α) = .60*.20 + .40*.95 = .50. Thus, if we sample repeatedly from the population of decisions 50% of them are rejections and 50% of them are non-rejections.

First, we focus only on the rejections. So, the probability of a rejection is now taken to be 1.0.  The posterior odds of the alternative to the null, given that the probability of a rejection is 1.0, is the prior likelihood ratio multiplied by the PLR: 1.5 * 16 = 24. Thus, we have a huge number of rejections (50% of our huge number of randomly sampled decisions) and within this huge number of rejections the proportion of rejections of false nulls is 24 times larger than the proportion of rejections of true nulls. The proportion of rejections of false nulls equals the posterior odds / (1 + posterior odds) = 24 / 25 = .96. (Interpretation: If we repeatedly sample a null-hypothesis from our huge number of rejected null-hypotheses, 96% of those samples are false null-hypotheses).

Second, we focus only on the non-rejections. Thus, the probability of a non-rejection is now taken to be 1.0. The posterior odds of the alternative to the null, given that the probability of a non-rejection is 1.0, is the prior odds multiplied by the NLR: 1.5 * 0.21 = 0.32. In other words, we have a huge number of non-rejections (50% of our huge sample of randomly selected decisions) and the proportion of non-rejections of false nulls is 0.32 times as large as the proportion of non-rejections of true nulls. The proportion of non-rejections of false nulls equals 0.32 / ( 1 + 0.32) =  .24. (Interpretation: If we repeatedly sample a null-hypothesis from our huge number of non-rejected hypotheses, 24% of them are false nulls).

So, based on the assumptions we made, NHST seems like a pretty good screening test, although in this example NHST is much better at detecting false null-hypothesis than ruling out false alternative hypotheses. But how about the question of decisions as evidence for the reality of an effect? I will first write a little bit about the interpretation of probabilities, then I will show you that decisions are not evidence.

Sometimes, these results are formulated as follows. The probability that the alternative is true given a decision to reject is .96 and the probability that the alternative hypothesis is true given a decision to not-reject  is .24.  If you want to correctly interpret such a statement, you have to keep in mind what “probability” means in the context of this statement, otherwise it is very easy to misinterpret the statement’s meaning. That is why I included interpretations of these results that are consistent with the meaning of the term probability as it used in our example. (In conceptual terms, the limit of the relative frequency of an event (such as reject or not-reject) as the number of random samples (the number of decisions) goes to infinity).

A common (I believe) misinterpretation (given the sampling context described above) is that rejecting a null-hypothesis makes the alternative hypothesis likely to be true. This misinterpretation is easily translated to the incorrect conclusion that a significant test result (that leads to a rejection) makes the alternative hypothesis likely to be true. Or, in other words, that a significant result is some sort of evidence for  the alternative hypothesis (or against the null-hypothesis).

The mistake can be described as confusing the probability of a single result with the long term (frequentist) probabilities associated with the decision or estimation procedure. For example, the incorrect interpretation of the p-value as the probability of a type I error or the incorrect belief that an obtained 95% confidence interval contains the true value of a parameter with  probability .95.

A quasi-simple example may serve to make the mistake clear. Suppose I flip a fair coin, keep the result hidden from you, and let you guess whether the result is heads or tails (we assume that the coin will not land on it’s side). What is the probability that your guess is correct?

My guess is that you believe that the probability that your guess is correct is .50. And my guess is also that you will find it hard to believe that you are mistaken. Well, you are mistaken if we define probabilities as long term relative frequencies. Why? The result is either heads or tails. If the result is heads, the long term relative frequency of that result is heads. That is to say, the result is a constant and constants do not vary in the long run. Your guess is also a constant, if you guess heads, it will stay heads in the long run. So, if the result is heads, the probability that your guess (heads) is correct is 100%, however, if your guess is tails, the probability of you being correct is 0%. Likewise, if the result is tails, it will stay tails forever, and the probability that your guess is correct is 0% if your guess is heads and 100% if your guess is tails. So, the probability that your guess is correct is either 0 or 1.0, depending on the result of the coin flip, and not .50.

The probability of guessing correct is .50, however, if we repeatedly (a huge number of times) play our game and both the result of the coin flip and your guess are the result of random sampling. Let’s assume that of all the guesses you do 50% are heads and 50% are tails.  In the long run, then, there is a probability of .25 of the result being heads and your guess being heads and a probability of .25 of the result being tails and your guess being tails. The probability of a correct decision is therefore, .25 + .25 = .50

Thus, if  both the results and your guesses are the result of random sampling and we repeated the game a huge number of times, the probability that you are correct is .50. But if we play our game only once, the probability of you being correct is 0 or 1.0, depending on the result of the coin flip.

Let’s return to the world of hypotheses and decisions. If we play the decision game once, the probability that your decision is correct is 0 or 1.0, depending on whether the null-hypothesis in question is true (with probability 0 or 1.0) or false (with probability 0 or 1.0). Likewise, the probability that the null-hypothesis is true given that you have rejected is also 0 or 1.0, depending on whether the null-hypothesis in question is true or false. But if we play the decision game a huge number of times, the probability that a null-hypothesis is false, given that you have decided to reject is .96 (in the context of the situation described above).

In sum, from the frequentist perspective we can only assign probabilities 0 or 1.0 to a single hypothesis given we have a made single decision about it, and this probability depends on whether that single hypothesis is true or false.  For this reason, a significant result cannot be magically translated to the probability that the alternative hypothesis is true given that the test result is significant. That probability is 0 or 1.0 and there’s nothing that can change that.

The consequence of all this is as follows. If we define the evidence for or against our alternative hypothesis in terms of the likelihood ratio of the alternative to the null-hypothesis after obtaining the evidence, no decision can serve as evidence if our decision procedure is based on frequentist probabilities. Decisions are not evidence.

Kline, R.B. (2013). Beyond significance testing. Statistics reform in the behavioral sciences. Second Edition. Washington: APA.