# stopping rule impact

May 8, 2014
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Here is a question from my friend Shravan Vasishth about the consequences of using a stopping rule:

Psycholinguists and psychologists often adopt the following type of data-gathering procedure: The experimenter gathers n data points, then checks for significance (p<0.05 or not). If it’s not significant, he gets more data (n more data points). Since time and money are limited, he might decide to stop anyway at sample size, say, some multiple of n.  One can play with different scenarios here. A typical n might be 10 or 15.

This approach would give us a distribution of t-values and p-values under repeated sampling. Theoretically, under the standard assumptions of frequentist methods, we expect a Type I error to be 0.05. This is the case in standard analyses (I also track the t-statistic, in order to compare it with my stopping rule code below).

Here’s a simulation showing what happens. I wanted to ask you whether this simulation makes sense. I assume here that the experimenter gathers 10 data points, then checks for significance (p<0.05 or not). If it’s not significant, he gets more data (10 more data points). Since time and money are limited, he might decide to stop anyway at sample size 60. This gives us p-values under repeated sampling. Theoretically, under the standard assumptions of frequentist methods, we expect a Type I error to be 0.05. This is the case in standard analyses:
##Standard:
pvals<-NULL
tstat_standard<-NULL
n<-10 # sample size
nsim<-1000 # number of simulations
stddev<-1 # standard dev
mn<-0 ## mean

for(i in 1:nsim){
samp<-rnorm(n,mean=mn,sd=stddev)
pvals[i]<-t.test(samp)$p.value tstat_standard[i]<-t.test(samp)$statistic}

## Type I error rate: about 5% as theory says:
table(pvals<0.05)[2]/nsim


But the situation quickly deteriorates as soon as adopt the strategy I outlined above:

pvals<-NULL
tstat<-NULL
## how many subjects can I run?
upper_bound<-n*6

for(i in 1:nsim){
## at the outset we have no significant result:
significant<-FALSE
## null hyp is going to be true,
## so any rejection is a mistake.
## take sample:
x<-rnorm(n,mean=mn,sd=stddev)
while(!significant & length(x)0.05){
## get more data:
x<-append(x,rnorm(n,mean=mn,sd=stddev))
## otherwise stop:
} else {significant<-TRUE}}
## will be either significant or not:
pvals[i]<-t.test(x)$p.value tstat[i]<-t.test(x)$statistic}


Now let’s compare the distribution of the t-statistic in the standard case vs with the above stopping rule. We get fatter tails with the above stopping rule, as shown by the histogram below.

Is this a correct way to think about the stopping rule problem?

To which I replied the following:

By adopting a stopping rule on a random iid sequence, you favour values in the sequence that agree with your stopping condition, hence modify the distribution of the outcome. To take an extreme example, if you draw N(0,1) variates until the empirical average is between -2 and 2, the average thus produced cannot remain N(0,1/n) but have a different distribution.

The t-test statistic you build from your experiment is no longer distributed as a uniform variate because of the stopping rule: the sample(x1,…,x10m) (with random size 10m [resulting from increases in the sample size by adding 10 more observations at a time] is distributed from

if 10m<60 [assuming the maximal acceptable sample size is 60] and from

otherwise. The histogram at the top of this post is the empirical distribution of the average of those observations, clearly far from a normal distribution.

Filed under: Books, R, Statistics, University life Tagged: bias, p-values, probability theory, stopping rule

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