Are two samples of size n/2 better than one of size n?

Today, on Twitter, I was involved in a discussion with statistical psychologist (or psychological statistician) Daniël Lakens on replication. Not to break the rule that any Twitter-discussion Daniël is involved in ends up in a blog-post, I’ve decided to write a blog-post on it myself.


Essentially, our discussion was about the following. Data was collected with a certain sample size n and subsequently some type of standard (frequentist) statistical test, such as a t-test, ANOVA or linear regression test was performed (and for sake of simplicity we assume that all statistical assumptions are met). Is there any benefit the following approach of splitting the data into two equal parts, such that you have a smaller sample and a replication of the test? One might think so, given that replication and reproducibility are the new hypes in psychological methodology.

However, in my opinion, the main strength or replication lies in having an experiment that took place in Laboratory A  replicated in Laboratory B. Perhaps the most obvious benefit of performing a replication is that you increase the sample size. If Laboratory A performed a study with n = 40, and you performed one with n = 40, then in the end you have n = 80. Obviously, this benefit is lost when you don’t really replicate, but cut your sample in half and call one half the replication. With this type of replication, you can check whether the significant result in Laboratory A was not simply due to coincidence (which happens α = 5% of times when there is no true effect).

Some other benefits of “real” replication are concerned with checking whether the experiment is reproducible and generalisable at all. If the experimenter used n = 40 local undergraduate students for his experiment (because it is so easy to oblige your students to be participants), it is of course unclear whether this result is generalisible to the population of interest (e.g. “everyone”). It helps if someone re-does the study with undergraduate students from another university. It is still very unclear whether the study is generalisable to non-students, but at least you can sort of find out whether students at different universities are similar. Again, this benefit only is there for real replications.


Let’s formalise the setting a bit and let’s keep things simple (it’s too sunny to stay too long behind the computer) and it doesn’t get much simpler than the one-sample t-test. Given is a random sample X1, …, Xn from a N(μ, σ2) distribution. Required is the test for H0: μ = 0 versus two-sided alternative and, specifically, the p-value of this test. For sake of simplicity assume that we are in the ideal world: the sample is truly random and the population distribution is indeed truly normal. Also, we assume that n is even (otherwise we can’t split it in exact halves).

Standard Approach (SA). The standard-approach would be to perform the standard t-test on the data. Any textbook on statistics will tell you how to do this.

Replication Approach (RA). The “replication”-approach would be to perform two t-tests; one on observation 1 up to n/2 and one on observation (n/2 + 1)up to n. This way we obtain two p-values which we need to combine into one overall p-value. For this, we can simply use Fisher’s method, which boils down to the following. If H0 is true, then both p-values are independent and uniformly distributed on [0, 1]. Standard distribution theory then provides that X = -2(ln(p1) + ln(p2)) follows a χ2-distribution with 4 degrees of freedom and for this distribution we can compute the p-value given X.

Answer using mathematical statistics

Now we have both approaches, we can return to the fundamental question: is there a benefit in applying RA over SA? The direct answer is no, there is not. For the given setting, the t-test is the so-called Uniformly Most Powerful Unbiased (UMPU) test (see, e.g., Lehmann, 1959, Testing Statistical Hypotheses). This means that (i) the test is unbiased (when there is no effect – H0 is true – the test rejects α = 5% of times) and (ii) the test is uniformly most powerful: no other test has higher power, whatever the circumstances. In laymen terms: under the settings of the experiment, no other test can perform better. This is obviously quite a good property for a test to have.  Both in general as now: it automatically answers our question. The replication approach is another test based on the same data and can therefore not perform better than the standard approach (it can, at best, perform just as well). This answer also holds true when we move away from the super-simplified t-test setting to ANOVA or linear regression: also there the default tests are UMPU.

Answer using simulations

The theory behind most powerful tests does answer the question “is there a benefit in the replication approach” (with “no”) but it does not quantify the difference between both approaches.

To this end, I ran the following simulation. For given settings of sample size n (either 40 or 80) and true population mean μ (from 0 to 1 in steps of 0.125), I’ve simulated 10,000 data sets of size n from a N(μ, 1) distribution. For each data set, I’ve computed the corresponding p-value for SA and RA. Furthermore, I’ve dichotomised these p-values into “significant”/”not-significant” based on α = 5%. R-code is provided at the bottom of this post.

Mean p-value for n = 40Let’s focus first on n = 40. Above, a comparison of the average p-value (over the 10,000) replications for the SA (black) and the RA (red). (Please note that the uncertainty due to simulation error is really small, since I work with 10,000 repetitions. At first, I’ve created this plot including 95% CI, but this interval was so narrow, it was often only one or two pixels wide.)

When μ = 0, then H0 is true: the p-values are distributed according to a U(0, 1) distribution, thus should have mean 1/2 and variance 1/12. Both SA and RA yield values very close to this (SA: mean = 0.5005, var = 0.0834; RA mean = 0.5004, var = 0.0833). So, both methods have a Type I error rate of (about) 5%, which is what you want.  When μ > 0, the alternative hypothesis is true, thus you hope to reject the null and you want small p-values. As expected, the larger μ, and thus the larger the effect size, the smaller the average p-value. The figure shows that the Standard Approach beats the Replication Approach.

n = 40, proportion significant resultsNext, we look at the proportion of results that are flagged as significant (at a nominal level of 5%). For μ = 0, you expect this to be 5% (the Type I Error Rate), and it is 5% for both SA as RA. For μ > 0, this proportion is 1 – the Type II Error Rate, or the power, and you expect it to go up when μ goes up. And it does. Again, it is clear that the Standard Approach performs better than the Replication Approach, especially for smaller effect sizes. (When the effect size is huge, then also clearly sub-optimal procedures have no problems with classifying the result as ‘significant’. The difference in power between SA and RA certainly is non-neglectible; it goes up to 0.117 10.8% when μ = 0.375 (in which case SA has power 0.640 and RA has power 0.523).replication3replication4The last two images are concerned with the simulations for n = 80. They show a similar pattern: the standard approach is indeed the better approach. Now, the maximal difference in power is 0.108 when μ = 0.25 (in which case SA has power 0.600 and RA has power 0.492).


This type of replication is not useful, at least not in the current setting. It would be more useful if one for instance seriously doubts the distributional assumption underlying the one-sample t-test or doubts the independence of observations. In such cases, non-parametric approaches could be preferred over parametric ones, and the Replication Approach applied here is a basic version of split-half cross-validation, a commonly used non-parametric technique.In the above, I’ve limited myself to the frequentist setting. However, in a Bayesian setting under similar circumstances, the RA would also not be beneficial. Just as in the frequentist setting, the Bayesian version for the t-test is developed to be uniformly optimal in some (Bayesian) sense. Other approaches, based on the same data, therefore can never be more optimal.

R code

Below is the R-code. The first part runs the simulations, which could take some time, and the second part creates the figures.

X           <- (0:8)/8
n           <- 40  
mu          <- 0   
repetitions <- 10^5
p.all.40    <- matrix(NA, nrow=9, ncol=repetitions)
p.split.40  <- p.all.40
p.all.80    <- matrix(NA, nrow=9, ncol=repetitions)
p.split.80  <- p.all.80
for(i in 1:repetitions){
  basedata <- rnorm(n,0,1)
  for(j in 0:8){
    data  <- basedata + j/8
    dataA <- data[1:(n/2)]
    dataB <- data[(n/2 +1):n]
    p.all.40[j+1,i] <- t.test(data,var.equal=TRUE)$p.value
    p.split.40[j+1,i] <- pchisq(-2*(log(t.test(dataA,
         var.equal=TRUE)$p.value) + log(t.test(dataB,
         var.equal=TRUE)$p.value)), df=4, lower.tail=FALSE)
n    <- 80  
for(i in 1:repetitions){
  basedata <- rnorm(n,0,1)
  for(j in 0:8){
    data  <- basedata + j/8
    dataA <- data[1:(n/2)]
    dataB <- data[(n/2 +1):n]
    p.all.80[j+1,i] <- t.test(data,var.equal=TRUE)$p.value
    p.split.80[j+1,i] <- pchisq(-2*(log(t.test(dataA,
         var.equal=TRUE)$p.value) + log(t.test(dataB,
         var.equal=TRUE)$p.value)), df=4, lower.tail=FALSE)
p.all       <- p.all.40 # or p.split.80; manually change
p.split     <- p.split.40 # same comment
issig.all   <- (p.all < .05)
issig.split <- (p.split < .05)
plot(X,apply(p.all,1,mean),type="b", ylab="mean p-value", 
  xlab=expression(mu), ylim=c(0,.5),main="n = ...",pch=19, 
lines(X,apply(p.split,1,mean), col=rgb(.5,0,0,.8),
legend("topright",c("Standard approach","Replication 
  approach"), col=c(rgb(0,.5,.5,.8), rgb(.5,0,0,.8)), 
  ylab="% significant results (alpha = 5%)",xlab="mu", 
  ylim=c(0,1.02), pch=19, yaxs="i", col=rgb(0,.5,.5,.8), 
  main = "n = ...")
legend("bottomright",c("Standard approach","Replication 
  approach"), col=c(rgb(0,.5,.5,.8), rgb(.5,0,0,.8)), 

University Council Elections: Vote Casper

(This post also appeared in Dutch)

Between 18 and 25 May, elections for the University Council of the university will take place.

I’m candidate on behalf of the Personnel Faction (List 1, #6) and hope to receive enough votes such that I can devote myself for a better working climate at the university, in the same way as I did in the past four years in the faculty council of the faculty of Behavioural and Social Sciences.

Below a slightly extended version of my motivation why I’m a candidate. In case you have any questions or comments, please leave them here, on Twitter, mail or in person.

Motivation and vision

The university is not a business, it is an academic institution. Academic thinking, not thinking in terms of profits, should therefore prevail in governance and personnel participation. A university is not a science factory where quality is measured fully through number of publications, impact factors, and – above all – whether you earn your own salary in grants. I’m convinced that governance with less focus on measurable performance indicators will lead, on average, to better research. Furthermore, it will certainly lead to a better working climate.

Academic education distinguishes itself from other types of (higher) education: not only do we expect students to gain skills and knowledge, we also expect them gain an academic attitude. For this, the university should create an atmosphere that invites students to develop themselves. Without academic freedom no academic research nor academic education. Finally, good teaching and research can only be obtained when this is coupled with good support.

The past four year I’ve been active in the Faculty Council of BSS, which I’ve chaired for two years. In that position, I’ve devoted myself to increase the work satisfaction of the personnel of the faculty. The council has written a report (79 pages) (in Dutch; link only available within BSS; in case you’re interested, drop me a mail) which was one of the reasons why the Faculty Board decided to adapt the Tenure Track-policy. Furthermore, I fight against governance based on silly numbers such as university rankings and publication indices. I support the RethinkRUG-movement.

Short Curriculum

Casper Albers is associate professor in statistics at the faculty of Behavioural and Social Sciences. He obtained degrees in econometrics and statistics and defended his PhD-thesis in mathematical statistics in 2003, all in Groningen. After a PostDoc in bioinformatics and a four-year research position at The Open University (UK), he returned to Groningen in 2009 for his current position. The past four years he was a member of the Faculty Council, which he chaired for two years. His research focusses on the development of models for longitudinal data, and the applications of these models in environmental and clinical psychology.

Universiteitsraadsverkiezingen 2015: Stem op Casper

(This post also appeared in English)

Tussen 18 mei 09:00 en 25 mei 17:00 kunnen medewerkers en studenten van de RUG stemmen voor de Universiteitsraad.

Ik ben kandidaat voor de Personeelsfractie en hoop dat komende week voldoende medewerkers op Lijst 1, Kandidaat 6 stemmen zodat ik me de komende twee jaar in kan zetten voor een beter werkklimaat aan de universiteit, net zoals ik dat de afgelopen vier jaar binnen de faculteitsraad heb gedaan voor de Faculteit GMW.

Hieronder een uitgebreide versie van de motivatie waarom ik mijzelf kandidaat gesteld heb. Mocht je vragen/opmerkingen hebben, stel ze gerust via de comments hieronder, twitter, mail of persoonlijk.

Motivatie en visie

De universiteit is geen bedrijf, maar een academische instelling. Dat roept om bestuur en medezeggenschap waar niet bedrijfsmatig denken maar academisch denken de boventoon voert. Een universiteit moet geen wetenschapsfabriek zijn waarbij kwaliteit volledig wordt afgemeten aan aantallen publicaties, impact factoren en – bovenal – of je je eigen salaris wel terugverdient aan beurzen. Ik ben ervan overtuigd dat een minder prestatiegericht beleid in de grote lijn tot betere onderzoeksresultaten zal leiden. Het zal sowieso leiden tot een beter werkklimaat.

Academisch onderwijs onderscheidt zich van ander (hoger) onderwijs doordat van de studenten verwacht wordt dat zij, naast kennis en vaardigheden vergaderen, zich ook bezig houden met intellectuele zelfontplooiing. Dit kan alleen wanneer daarvoor de juiste atmosfeer geschapen wordt. Zonder academische vrijheid geen academisch onderzoek noch academisch onderwijs. Goed onderzoek en onderwijs kan, tenslotte, alleen plaatsvinden wanneer deze processes goed gestroomlijnd ondersteund worden.

De afgelopen vier jaar ben ik actief geweest binnen de Faculteitsraad GMW, waarvan twee jaar als voorzitter. Vanuit die functie heb ik me uitvoerig ingezet voor de werktevredenheid van de medewerkers. Dit heeft geleid tot een onderzoeksrapport van 79 pagina’s (link alleen beschikbaar voor GMW-medewerkers. Andere geïnteresseerden kunnen me mailen voor een kopie). Conclusies van ons onderzoek waren onder andere dat het wetenschappelijk personeel bij GMW gemiddeld zo’n 6,8 uur per week overwerkt en dat de baanonzekerheid ten gevolge van o.a. willekeur bij het toekennen van externe financiëring als frustrerend werd ervaren. Dit rapport was mede aanleiding voor het Faculteitsbestuur om de Tenure Track-notitie te herzien. Rond deze herziening is het ons gelukt om het FB ervan te overtuigen dat een tijdelijk contract van vier jaar (i.p.v. zes) voldoende is om in te schatten of een medewerker goed genoeg is voor een vast contract, alsmede dat een aanstelling op het niveau van Universitair Docent voldoende kan zijn voor een vast contract. Helaas wou het College van Bestuur op dit moment deze wijzigingen nog niet honoreren.

Wie beter onderwijs wil, moet er meer geld voor overhebben.
Wie beter onderwijs wil, moet er meer geld voor overhebben. – Ingezonden brief, De Volkskrant, 9 augustus 2014

Een ander punt waar ik me de afgelopen jaren druk over heb gemaakt is de waanzin rond beleidsafstemming rond rankings (zowel universitaire rankings, als persoonlijke rankings zoals de H-index).

Er waait inmiddels een andere wind in academisch Nederland. Na de Maagdenhuisbezetting, is nu ook in Groningen RethinkRUG actief – de open brief heb ik vanzelfsprekend ook getekend. Op facultair niveau zijn er dus al wijzigingen zichtbaar, op universitair niveau gaat dit trager – om over de snelheid in Den Haag nog maar te zwijgen. Hopelijk kan ik in 2015-2017 meehelpen die wind de juiste kant – meer academische vrijheid voor medewerkers én studenten – op te laten waaien.

Waarom ik voor De Personeelsfractie gekozen heb

Zoals bekend, doen er twee personeelspartijen mee aan de verkiezingen: “De Personeelsfractie” en “De Personeelsfractie voor de Wetenschap”.  Inhoudelijk zijn er weinig verschillen tussen beide partijen. De PvdW heeft overal posters hangen met leuzen als “Minder werkdruk”, “Minder bureaucratie” en “Meer werkzekerheid”.  Dat zijn nobele doelen en ik hoop van harte dat deze partij met de zetels die de kiezer haar zal geven zal strijden op deze doelen tot stand te laten komen. Het zijn echter geen doelen die exclusief de PvdW toebehoren; beide fracties pleiten hier voor. Er waren de afgelopen twee jaar wel enkele subtiele verschillen tussen de partijen (zie het verkiezingsdebat tussen Bart Beijer en Mathieu Paapst), maar beide partijen komen op voor het personeel.

Voor mij was de hoofdreden om voor De Personeelsfractie te kiezen dat deze fractie de hele universiteit vertegenwoordigt. De PvdW heeft drie kandidaten, twee hoogleraren en een UD) uit twee faculteiten. De Personeelsfractie heeft veertien kandidaten.  Deze kandidaten komen van zes verschillende faculteiten en bestaan uit promovendi, U(H)D, hoogleraren én ondersteunend personeel. Door deze universiteitsbrede basis, is De Personeelsfractie in staat om daadwerkelijk namens het gehele personeel te spreken.

Beknopt cv

Casper Albers is UHD statistiek bij de faculteit Gedrags- en Maatschappijwetenschappen. In Groningen heeft hij achtereenvolgens een propedeuse econometrie (1995) en doctoraal statistiek (1998) behaald waarna hij in 2003 in de wiskundige statistiek promoveerde. Na een PostDoc-positie in bioinformatica en vier jaar onderzoek bij de Open University in Engeland is Casper in 2009 bij GMW terecht gekomen. De afgelopen vier jaar zat hij in de Faculteitsraad, waarvan twee jaar als voorzitter. Caspers onderzoek richt zich op de ontwikkeling van modellen voor longitudinale data en de toepassing hiervan in milieu- en klinische psychologie. Meer informatie is op mijn homepage te vinden.