# Why does collecting data until finding a significant result increase Type I error rate?

I was wondering exactly why collecting data until a significant result (e.g., $p \lt .05$) is obtained (i.e., p-hacking) increases the Type I error rate?

I would also highly appreciate an R demonstration of this phenomenon.

• You probably mean "p-hacking," because "harking" refers to "Hypothesizing After Results are Known" and, although that could be considered a related sin, it's not what you seem to be asking about.
– whuber
Oct 26, 2017 at 17:35
• Once again, xkcd answers a good question with pictures. xkcd.com/882 Oct 26, 2017 at 21:02
• @Jason I have to disagree with your link; that doesn't talk about cumulative collection of data. The fact that even cumulative collection of data about the same thing and using all data you have to compute the $p$-value is wrong is much more nontrivial than the case in that xkcd.
– JiK
Oct 27, 2017 at 9:35
• @JiK, fair call. I was focused on the "keep trying until we get a result we like" aspect, but you're absolutely correct, there's much more to it in the question at hand. Oct 27, 2017 at 11:04
• @whuber and user163778 gave very similar replies as discussed for the practically identical case of "A/B (sequential) testing" in this thread: stats.stackexchange.com/questions/244646/… There, we argued in terms of Family Wise Error rates and necessity for p-value adjustment in repeated testing. This question in fact can be looked at as a repeated testing problem! Oct 30, 2017 at 20:01

The problem is that you're giving yourself too many chances to pass the test. It's just a fancy version of this dialog:

I'll flip you to see who pays for dinner.

Rats, you won. Best two out of three?

To understand this better, consider a simplified--but realistic--model of this sequential procedure. Suppose you will start with a "trial run" of a certain number of observations, but are willing to continue experimenting longer in order to get a p-value less than $0.05$. The null hypothesis is that each observation $X_i$ comes (independently) from a standard Normal distribution. The alternative is that the $X_i$ come independently from a unit-variance normal distribution with a nonzero mean. The test statistic will be the mean of all $n$ observations, $\bar X$, divided by their standard error, $1/\sqrt{n}$. For a two-sided test, the critical values are the $0.025$ and $0.975$ percentage points of the standard Normal distribution, $Z_\alpha=\pm 1.96$ approximately.

This is a good test--for a single experiment with a fixed sample size $n$. It has exactly a $5\%$ chance of rejecting the null hypothesis, no matter what $n$ might be.

Let's algebraically convert this to an equivalent test based on the sum of all $n$ values, $$S_n=X_1+X_2+\cdots+X_n = n\bar X.$$

Thus, the data are "significant" when

$$\left| Z_\alpha\right| \le \left| \frac{\bar X}{1/\sqrt{n}} \right| = \left| \frac{S_n}{n/\sqrt{n}} \right| = \left| S_n \right| / \sqrt{n};$$

that is,

$$\left| Z_\alpha\right| \sqrt{n} \le \left| S_n \right| .\tag{1}$$

If we're smart, we'll cut our losses and give up once $n$ grows very large and the data still haven't entered the critical region.

This describes a random walk $S_n$. The formula $(1)$ amounts to erecting a curved parabolic "fence," or barrier, around the plot of the random walk $(n, S_n)$: the result is "significant" if any point of the random walk hits the fence.

It is a property of random walks that if we wait long enough, it's very likely that at some point the result will look significant.

Here are 20 independent simulations out to a limit of $n=5000$ samples. They all begin testing at $n=30$ samples, at which point we check whether the each point lies outside the barriers that have been drawn according to formula $(1)$. From the point at which the statistical test is first "significant," the simulated data are colored red.

You can see what's going on: the random walk whips up and down more and more as $n$ increases. The barriers are spreading apart at about the same rate--but not fast enough always to avoid the random walk.

In 20% of these simulations, a "significant" difference was found--usually quite early on--even though in every one of them the null hypothesis is absolutely correct! Running more simulations of this type indicates that the true test size is close to $25\%$ rather than the intended value of $\alpha=5\%$: that is, your willingness to keep looking for "significance" up to a sample size of $5000$ gives you a $25\%$ chance of rejecting the null even when the null is true.

Notice that in all four "significant" cases, as testing continued, the data stopped looking significant at some points. In real life, an experimenter who stops early is losing the chance to observe such "reversions." This selectiveness through optional stopping biases the results.

In honest-to-goodness sequential tests, the barriers are lines. They spread faster than the curved barriers shown here.

library(data.table)
library(ggplot2)

alpha <- 0.05   # Test size
n.sim <- 20     # Number of simulated experiments
n.buffer <- 5e3 # Maximum experiment length
i.min <- 30     # Initial number of observations
#
# Generate data.
#
set.seed(17)
X <- data.table(
n = rep(0:n.buffer, n.sim),
Iteration = rep(1:n.sim, each=n.buffer+1),
X = rnorm((1+n.buffer)*n.sim)
)
#
# Perform the testing.
#
Z.alpha <- -qnorm(alpha/2)
X[, Z := Z.alpha * sqrt(n)]
X[, S := c(0, cumsum(X))[-(n.buffer+1)], by=Iteration]
X[, Trigger := abs(S) >= Z & n >= i.min]
X[, Significant := cumsum(Trigger) > 0, by=Iteration]
#
# Plot the results.
#
ggplot(X, aes(n, S, group=Iteration)) +
geom_path(aes(n,Z)) + geom_path(aes(n,-Z)) +
geom_point(aes(color=!Significant), size=1/2) +
facet_wrap(~ Iteration)

• +1. Does any given random walk eventually cross the barriers with probability 1? I know that the expected distance after $n$ steps is $\mathcal O(\sqrt{n})$ and I looked up now that the proportionality constant is $\sqrt{2/\pi}$, which is less than 1.96. But I am not sure what to make of it. Oct 26, 2017 at 20:21
• @amoeba That is a great question, which I tried my best to dodge :-). If I could have computed the answer quickly (or knew it offhand) I would have posted it. Unfortunately I'm too busy to address it analytically right now. The longest simulation I have done was 1,000 iterations looking out as far as $n=5,000,000$ with $\alpha=0.05$. The proportion of "significant" results seems to stabilize near $1/4$.
– whuber
Oct 26, 2017 at 21:06
• The question about the probability of hitting the $\alpha=0.05$ boundary is interesting. I imagine that Einsteins theory of Brownian motion, relating it to a diffusion equation, could be an interesting angle. We have a distribution function spreading out with a rate $\sim \sqrt{n}$ and a "loss of particles" equal to half the value of the distribution function at this boundary (half moves away from zero, over the border, the other half goes back). As this distribution function spreads out, and gets thinner, the "loss" becomes less. I imagine this effectively will create a limit, ie this 1/4. Oct 27, 2017 at 1:41
• Intuitive reason why you'll get a $p<0.05$ at some point almost surely: Let $n_1=10$ and $n_{k+1}=10^{n_k}$. The $p$-value after the first $n_{k+1}$ trials is pretty much independent of the $p$-value after the first $n_k$ trials. So you'll have an infinite number of "almost" independent $p$-values, so one of them is guaranteed to be $<0.05$. Of course, the real convergence is much faster than this argument says. (And if you don't like $10^{n_k}$, you may try $A(n_k)$ or $\mathrm{BB}(n_k)$...)
– JiK
Oct 27, 2017 at 11:34
• @CL. I anticipated your objection several years ago: 17 is my public seed. In fact, in early (much longer) trials I was consistently getting greater rates of significance substantially larger than 20%. I set the seed at 17 to create the final image and was disappointed that the effect was not so dramatic. C'est la vie. A related post (illustrating your point) is at stats.stackexchange.com/a/38067/919.
– whuber
Oct 27, 2017 at 14:05

People who are new to hypothesis testing tend to think that once a p value goes below .05, adding more participants will only decrease the p value further. But this isn't true. Under the null hypothesis, a p value is uniformly distributed between 0 and 1 and can bounce around quite a bit in that range.

I've simulated some data in R (my R skills are quite basic). In this simulation, I collect 5 data points - each with a random selected group membership (0 or 1) and each with a randomly selected outcome measure ~N(0,1). Starting on participant 6, I conduct a t-test at every iteration.

for (i in 6:150) {
df[i,1] = round(runif(1))
df[i,2] = rnorm(1)
p = t.test(df[ , 2] ~ df[ , 1], data = df)$p.value df[i,3] = p }  The p values are in this figure. Notice that I find significant results when the sample size is around 70-75. If I stop there, I'll end up beleiving that my findings are significant because I'll have missed the fact that my p values jumped back up with a larger sample (this actually happened to me once with real data). Since I know both populations have a mean of 0, this must be a false positive. This is the problem with adding data until p < .05. If you add conduct enough tests, p will eventually cross the .05 threshold and you can find a significant effect is any data set. • Thanks but your R code doesn't run at all. – Reza Oct 26, 2017 at 22:49 • @Reza you'd need to create df first (preferably at its final size). Since the code starts writing at row 6 the implication (which fits with the text of the answer) is that df already exists with 5 rows already filled in. Maybe something like this was intended: n150<-vector("numeric",150); df<-data.frame(gp=n150,val=n150,pval=n150); init<-1:5; df[init,1]<-c(0,1,0,1,0); df[init,2]<-rnorm(5) (then run the code above) then perhaps: plot(df$pv[6:150]) Oct 27, 2017 at 4:45
• @user263778 very focused useful and pertinent answer .But there is too much confusion about interpreting the p-value so called - dancing beauty.
– user10619
Oct 27, 2017 at 8:31
• @user163778 - you should include the code to initialize everything too Oct 27, 2017 at 19:41

This answer only concerns the probability of ultimately getting a "significant" result and the distribution of the time to this event under @whuber's model.

As in the model of @whuber, let $$S(t)=X_1 + X_2 + \dots + X_t$$ denote the value of the test statistic after $$t$$ observations have been collected and assume that the observations $$X_1,X_2,\dots$$ are iid standard normal. Then $$S(t+h)|S(t)=s_0 \sim N(s_0, h), \tag{1}$$ such that $$S(t)$$ behaves like a continuous-time standard Brownian motion, if we for the moment ignore the fact that we have a discrete-time process (left plot below).

Let $$T$$ denote the first passage time of $$S(t)$$ across the the time-dependent barriers $$\pm z_{\alpha/2}\sqrt{t}$$ (the number of observations needed before the test turns significant).

Consider the transformed process $$Y(\tau)$$ obtained by scaling $$S(t)$$ by its standard deviation at time $$t$$ and by letting the new time scale $$\tau=\ln t$$ such that $$Y(\tau)=\frac{S(t(\tau))}{\sqrt{t(\tau)}}=e^{-\tau/2}S(e^\tau). \tag{2}$$ It follows from (1) and (2) that $$Y(\tau+\delta)$$ is normally distributed with \begin{align} E(Y(\tau+\delta)|Y(\tau)=y_0) &=E(e^{-(\tau+\delta)/2}S(e^{\tau+\delta})|S(e^\tau)=y_0e^{\tau/2}) \\&=y_0e^{-\delta/2} \tag{3} \end{align} and \begin{align} \operatorname{Var}(Y(\tau+\delta)|Y(\tau)=y_0) &=\operatorname{Var}(e^{(\tau+\delta)/2}S(e^{\tau+\delta})|S(e^\tau)=y_0e^{\tau/2}) \\&=1-e^{-\delta}, \tag{4} \end{align} that is, $$Y(\tau)$$ is a zero-mean Ornstein-Uhlenbeck (O-U) process with a stationary variance of 1 and return time 2 (right plot below). An almost identical transformation is given in Karlin & Taylor (1981), eq. 5.23.

For the transformed model, the barriers become time-independent constants equal to $$\pm z_{\alpha/2}$$. It is then known (Nobile et. al. 1985; Ricciardi & Sato, 1988) that the first passage-time $$\mathcal{T}$$ of the O-U process $$Y(\tau)$$ across these barriers is approximately exponentially distributed with some parameter $$\lambda$$ (depending on the barriers at $$\pm z_{\alpha/2}$$) (estimated to $$\hat\lambda=0.125$$ for $$\alpha=0.05$$ below). There is also an extra point mass in of size $$\alpha$$ in $$\tau=0$$. "Rejection" of $$H_0$$ eventually happens with probability 1. Hence, $$T=e^\mathcal{T}$$ (the number of observations that needs to be collected before getting a "significant" result) is approximately Pareto distributed with density $$f_T(t)=f_\mathcal{T}(\ln t)\frac{d\tau}{dt}=\lambda/t^{\lambda+1}$$. The expected value is $$ET\approx 1+(1-\alpha)\int_0^\infty e^\tau \lambda e^{-\lambda \tau}d\tau.\tag{5}$$ Thus, $$T$$ has a finite expectation only if $$\lambda>1$$ (for sufficiently large levels of significance $$\alpha$$).

The above ignores the fact that $$T$$ for the real model is discrete and that the real process is discrete- rather than continuous-time. Hence, the above model overestimates the probability that the barrier has been crossed (and underestimates $$ET$$) because the continuous-time sample path may cross the barrier only temporarily in-between two adjacent discrete time points $$t$$ and $$t+1$$. But such events should have negligible probability for large $$t$$.

The following figure shows a Kaplan-Meier estimate of $$P(T>t)$$ on log-log scale together with the survival curve for the exponential continuous-time approximation (red line).

R code:

# Fig 1
par(mfrow=c(1,2),mar=c(4,4,.5,.5))
set.seed(16)
n <- 20
npoints <- n*100 + 1
t <- seq(1,n,len=npoints)
subset <- 1:n*100-99
deltat <- c(1,diff(t))
z <- qnorm(.975)
s <- cumsum(rnorm(npoints,sd=sqrt(deltat)))
plot(t,s,type="l",ylim=c(-1,1)*z*sqrt(n),ylab="S(t)",col="grey")
points(t[subset],s[subset],pch="+")
tau <- log(t)
y <- s/sqrt(t)
plot(tau,y,type="l",ylim=c(-2.5,2.5),col="grey",xlab=expression(tau),ylab=expression(Y(tau)))
points(tau[subset],y[subset],pch="+")
abline(h=c(-z,z))

# Fig 2
nmax <- 1e+3
nsim <- 1e+5
alpha <- .05
t <- numeric(nsim)
n <- 1:nmax
for (i in 1:nsim) {
s <- cumsum(rnorm(nmax))
t[i] <- which(abs(s) > qnorm(1-alpha/2)*sqrt(n))[1]
}
delta <- ifelse(is.na(t),0,1)
t[delta==0] <- nmax + 1
library(survival)
par(mfrow=c(1,1),mar=c(4,4,.5,.5))
plot(survfit(Surv(t,delta)~1),log="xy",xlab="t",ylab="P(T>t)",conf.int=FALSE)

• Thanks! Do you have any (standard) references for these results? For instance, why is the Y process an Ornstein-Uhlenbeck and where can we find the passage time result stated? Oct 28, 2017 at 8:03
• I haven't seen this transformation anywhere else but I believe (3) and (4) that follows easily from (1) and (2) and normality completely characterises the O-U process. Google scholar returns a lot of results on approximate exponentiality of first-passage time distributions for the O-U process. But I believe that $\mathcal{T}$ in this case (within the continuous-time approximation) is exactly exponentially distributed (except for an extra point mass in $\tau=0$) because $Y(0)$ comes from the stationary distribution of the process. Oct 28, 2017 at 8:47
• @Grassie Also see math.stackexchange.com/questions/1900304/… Oct 28, 2017 at 9:16
• @Grassie Actually, my argument based on memorylessness was flawed. The durations of excursions away from the boundaries are not exponentially distributed. Hence, based on the same argument as in stats.stackexchange.com/questions/298828/…, even though $Y(0)$ comes from the stationary distribution, the first passage time is not exactly exponentially distributed. Oct 28, 2017 at 10:08
• @Grassie I just came across the transformation of Brownian motion to O-U in Karlin & Taylor's second course in stochastic procesess. Feb 12, 2020 at 12:12

It needs to be said that the above discussion is for a frequentist world view for which multiplicity comes from the chances you give data to be more extreme, not from the chances you give an effect to exist. The root cause of the problem is that p-values and type I errors use backwards-time backwards-information flow conditioning, which makes it important "how you got here" and what could have happened instead. On the other hand, the Bayesian paradigm encodes skepticism about an effect on the parameter itself, not on the data. That makes each posterior probability be interpreted the same whether you computed another posterior probability of an effect 5 minutes ago or not. More details and a simple simulation may be found at http://www.fharrell.com/2017/10/continuous-learning-from-data-no.html

• Let's imagine a lab led by Dr B, who is a devout Bayesian. The lab is studying social priming and has produced a steady stream of papers showing various effects of priming, each time supported by Bayes factor BF>10. If they never do sequential testing, it looks pretty convincing. But let's say I learn that they always do sequential testing and keep getting new subjects until they obtain BF>10 in favour of priming effects. Then clearly this whole body of work is worthless. The fact that they do sequential testing + selection makes a huge difference, no matter if it's based on p-values of BF. Oct 29, 2017 at 14:39
• I don't use Bayes' factors. But had they used posterior probabilities and had run each experiment until the posterior probability of a positive effect $\geq 0.95$, there would be absolutely nothing wrong with these probabilities. Look at the quote at the start of my blog article - see the link in my answer above. The degree of belief about the priming effect comes from the data and the prior belief. If you (like me) are very dubious of such priming effects, you'd better use a fairly skeptical prior when computing the posterior probabilities. That's it. Oct 29, 2017 at 20:20
• I read your blog post, noticed the quote, and looked at a similar paper (Optional stopping: No problem for Bayesians) that somebody else linked to in the comments to another answer. I still don't get it. If the "null" (absent priming effects) is true, then if Dr B is willing to sample long enough, he will be able to get posterior probability >0.95 every single time he runs the experiment (exactly as Dr F would be able to get p<0.05 every single time). If this is "absolutely nothing wrong" then I don't know what is. Oct 29, 2017 at 20:42
• Well, I dispute this "bigger point". I don't think this is true. As I keep repeating, under the null of zero effect and with any given prior (let's say some broad continuous prior centered at zero), repeated sampling will always sooner or later yield >0.98 posterior probability concentrated above zero. A person who is sampling until this happens (i.e. applying this stopping rule), will be wrong every single time. How can you say that this person will be wrong only 0.02 of the time? I don't understand. Under these particular circumstances, no he will not, he will always be wrong. Oct 30, 2017 at 21:49
• I don't think I am. My bigger point is that it is unfair & inconsistent to simultaneously blame frequentist procedures for suffering from sequential testing and defend Bayesian procedures as unaffected by sequential testing. My point (which is a mathematical fact) is that they both are affected in exactly the same way, meaning that sequential testing can increase Bayesian type I error all the way to 100%. Of course if you say that you are, as a matter of principle, not interested in type I error rates, then it's irrelevant. But then frequentist procedures should not be blamed for that either. Oct 31, 2017 at 21:31

We consider a researcher collecting a sample of size $n$, $x_1$, to test some hypothesis $\theta=\theta_0$. He rejects if a suitable test statistic $t$ exceeds its level-$\alpha$ critical value $c$. If it does not, he collects another sample of size $n$, $x_2$, and rejects if the test rejects for the combined sample $(x_1,x_2)$. If he still obtains no rejection, he proceeds in this fashion, up to $K$ times in total.

This problem seems to already have been addressed by P. Armitage, C. K. McPherson and B. C. Rowe (1969), Journal of the Royal Statistical Society. Series A (132), 2, 235-244: "Repeated Significance Tests on Accumulating Data".

The Bayesian point of view on this issue, also discussed here, is, by the way, discussed in Berger and Wolpert (1988), "The Likelihood Principle", Section 4.2.

Here is a partial replication of Armitage et al's results (code below), which shows how significance levels inflate when $K>1$, as well as possible correction factors to restore level-$\alpha$ critical values. Note the grid search takes a while to run---the implementation may be rather inefficient.

Size of the standard rejection rule as a function of the number of attempts $K$

Size as a function of increasing critical values for different $K$

Adjusted critical values to restore 5% tests as a function of $K$

reps <- 50000

K <- c(1:5, seq(10,50,5), seq(60,100,10)) # the number of attempts a researcher gives herself
alpha <- 0.05
cv <- qnorm(1-alpha/2)

grid.scale.cv <- cv*seq(1,1.5,by=.01) # scaled critical values over which we check rejection rates
max.g <- length(grid.scale.cv)
results <- matrix(NA, nrow = length(K), ncol=max.g)

for (kk in 1:length(K)){
g <- 1
dev <- 0
K.act <- K[kk]
while (dev > -0.01 & g <= max.g){
rej <- rep(NA,reps)
for (i in 1:reps){
k <- 1
accept <- 1
x <- rnorm(K.act)
while(k <= K.act & accept==1){
# each of our test statistics for "samples" of size n are N(0,1) under H0, so just scaling their sum by sqrt(k) gives another N(0,1) test statistic
rej[i] <- abs(1/sqrt(k)*sum(x[1:k])) > grid.scale.cv[g]
accept <- accept - rej[i]
k <- k+1
}
}
rej.rate <- mean(rej)
dev <- rej.rate-alpha
results[kk,g] <- rej.rate
g <- g+1
}
}
plot(K,results[,1], type="l")
matplot(grid.scale.cv,t(results), type="l")
abline(h=0.05)