Statisticians have devised numerous statistical tests for deciding whether a replication passes or fails, thus validating of refuting the original result. I call these tests “measures”. Elsewhere I report results from simulating these tests across a range of conditions. Here I describe the measures themselves and provide implementation details for ones that aren’t obvious.


Various authors have proposed tests for deciding whether a replication succeeds or fails. These include significance testing of the replica study, seeing whether the observed effect size of one study falls in the confidence or prediction interval of the other, checking whether the confidence or prediction intervals of the two studies overlap, significance testing of the studies combined via meta-analysis, and comparing the observed effect size of the repeated study with a “small telescope” threshold deemed to be the smallest true effect size the original study could have plausibly detected.

I simulated these rules (and more) across many replication conditions and computed false positive and false negative rates for conditions of interest. I reported these results in a working paper, Systematic Replication Has Limited Power to Detect Bad Science, a shorter blog post, Systematic Replication May Make Many Mistakes, and a supplement to the blog post.

In this document I describe the measures themselves, providing implementation details where appropriate. The code implements 28 measures. These include all replication rules I found in the literature or blogosphere that I was able to implement with adequate performance, plus ones I added to satisfy my sense of completeness, and a few I needed for technical reasons. The Measures section below has them all, but here a few to give you a flavor.

Several measures depends on nuances of the noncentral t-distribution. The usual treatment parameterizes this distribution by degrees of freedom and a noncentrality parameter, and operates on the t-statistic. I prefer to work with more concrete notions: sample size (instead of degrees of freedom), (presumed) population effect size (instead of the noncentrality parameter), and standardized observed effect size (instead of the t-statistic). For the limited context of my simulation, it’s easy to convert between the concepts. I developed software that repackages base R’s t-distribution functions to use my preferred concepts; I call this the noncentral d2t-distribution. To be clear: the d2t-distribution is not a new distribution in any sense; it merely repackages the t-distribution. I describe the d2t-distribution software in Noncentral d2t-Distribution Applied to Measures of Replication Success. The d2t software does much of the heavy lifting for the measures.


I consider a basic replication scheme in which each original study is repeated once.

The software first simulates studys across a range of conditions, then combines pairs of studies into pairwise replications, applies rules (the measures) for deciding which pairwise replications pass, and finally computes true and false positive and negative rates for measures and conditions of interest.

The studies are simple two group comparisons parameterized by sample size \(n\) and population effect size \(d_{pop}\) (\(d_{pop}\ge0\)). For each study, I generate two groups of random numbers, each of size \(n\). One group, group0, comes from a standard normal distribution with \(mean=0\); the other, group1, is from a standard normal distribution with \(mean=d_{pop}\). When I need to be pedantic, I use the term study set for the ensemble of studies for a given combination of \(n\) and \(d_{pop}\).

To generate pairwise replications, I consider all (ordered) pairs of study sets. For each pair, the software permutes the instances of each study, then combines the instances row-by-row. It is often convenient to think of the first study of the pair as the original and the second as the replication, but the software doesn’t rely on this distinction. A pairwise replication set is the ensemble of pairwise replication instances for a given pair of study sets. Four variables parameterize each pairwise replication set: \(n1\), \(n2\), \(d1_{pop}\), \(d2_{pop}\). These are, naturally enough, the sample and population effect sizes for the two study sets.

After forming the pairwise replications, I apply the measures. Each measure takes a pairwise replication set as input and returns a vector of boolean values telling which instances pass or fail. The result is a boolean matrix whose rows represent replication instances and columns represent the measures’ results.

The next step summarizes the results by counting the number of true results for each measure in the replication set. I compute counts for the measures individually and a few combinations of interest. The combinations are

To get pass rates, I divide the counts by the number of instances satisfying some baseline condition. Usually, the baseline condition is the one called baseline above (hence the name), but it can be the null condition (in which case the denominator is the total number of instances per set, i.e., \(10^4\) by default) or either of the other named conditions above.

The final step is to convert pass rates into true positive, false positive, true negative, and false negative rates. This requires a definition of true and false instances, in other words, an explicit statement as to which pairwise replication instances represent replications that should succeed vs. ones that should fail. I call these correctness criteria. See the Correctness Criteria section of the working paper for a discussion.


The table below lists the 28 measures implemented by the software. Each measure applies to a pairwise replication instance (although some only depend on one study of the pair). Measures and the subsequent discussion use the following notation.

Caution The notation d is potentially ambiguous. When used in measures it means \(d_{sdz}\), the standardized observed effect size. Elsewhere it may \(d_{pop}\), the population effect size. I try to be clear in the text.

measure meaning
sig1 pval1 is significant
sig2 pval2 is significant
sigm pvalm is significant
sdir d1 and d2 are in same direction, i.e., have same sign
d1.c2 d1 is contained in c2
d2.c1 d2 is contained in c1 d1 is contained in cm d2 is contained in cm
dm.c1 dm is contained in c1
dm.c2 dm is contained in c2
c1.c2 c1 and c2 overlap c1 and cm overlap c2 and cm overlap
d1.p2 d1 is contained in p2
d2.p1 d2 is contained in p1
dm.p1 dm is contained in p1
dm.p2 dm is contained in p2
p1.p2 p1 and p2 overlap
d1.scp2 d1 exceeds scp2
d2.scp1 d2 exceeds scp1; note that Uri’s published small telescope method is this one and requires that sig2 also hold
dm.scp2 dm exceeds scp2
dm.scp1 dm exceeds scp1
d1.scpd2 d1 exceeds scpd2
d2.scpd1 d2 exceeds scpd1
dm.scpd2 dm exceeds scpd2
dm.scpd1 dm exceeds scpd1
big1 \(d1 \ge d2\)
big2 \(d2 \ge d1\)

Precursor Statistics

Performance is critical, because I apply the measures to many, many replications (more than 100 million in default full runs). The code computes precursor statistics that make the measures very fast. It computes each precursor statistic as early as possible in the pipeline: it computes what it can before doing any simulations, then what it can on each simulated study individually, and finally statistics that depend on the actual replications. I took care to implement the code in a vectorized fashion to operate on individual studies and replications in a single gulp.

Statistics computed before simulation

Statistics computed before the simulation cannot depend on the data, of course. In addition to values that truly do not depend on the data (notably Uri Simonsohn’s small telescope thresholds), I precompute expensive statistics on a parameter grid, then later interpolate to get the values for specific data. For interpolation, I use aspline from the akima package.

Small telescope thresholds

Uri Simonsohn’s small telescope thresholds depend only on the sample sizes of the original and replica studies. In Uri’s words, the method “evaluates the original study’s design rather than its result — the telescope rather than the planet”. I extended Uri’s method to take into account the observed effect size in the original study. This started out as my misunderstanding of Uri’s published method. Since I had the code, I decided to keep it.

Uri’s method first computes the smallest population effect size that would confer 33% power to the original study for seeing the smallest possible significant effect; Uri calls this \(d_{33\%}\). Then it computes the smallest effect size the replica study could see consistent with the hypothesis that the true effect size is \(\ge d_{33\%}\). I call this \(d_{close}\). If the replica observes an effect size \(\ge d_{close}\), the method deems it close enough to the original effect size to be valid; conversely, if the replica effect size is \(< d_{close}\), the method deems it too small to have been really detected by the original study.

My extended (effect size dependent) method differs only in the first step: it computes the smallest population effect size that would confer adequate power (default 33% as in Uri’s method) for the original study to see the original effect size rather than the smallest possible significant effect size.

Notation for small telescope thresholds

math code meaning
\(n1\), \(n2\) n1, n2 sample sizes of the two studies
\(pwr\) scope.power required power of original study; Uri fixes this at 33%
\(\alpha\) sig.level significance level
\(d_{sig}\) d.sig minimum significant effect size for a sample size of \(n1\); used in Uri’s version of the method
\(d1_{sdz}\) d1.sdz observed effect size in the original study; used in my effect size dependent version of the method
\(d_{scope}\) d.scope minimum population effect size that would confer \(pwr\) power to the original study; Uri calls this \(d_{33\%}\)
\(d_{close}\) d.close minimum acceptable observed effect size in the replica

Functions from d2t document

These functions use the identities \(d=t\sqrt{2/n}\) and \(t=d\sqrt{n/2}\) to convert between standardized effect sizes (\(d\)) and t-statistics (\(t\)) then use base R’s pt functions to convert t-statistics into p-values.

  • pval2d converts p-value to standardized effect size
  • p_d2t() is a wrapper for R’s pt() that expresses the t-distribution cumulative probability in \(d\) units


\[\begin{aligned} \bf{\text{Uri's method}}& \\ d_{sig}&=pval2d(n1,\alpha) \\ d_{scope}&=d0 \mid p\_d2t(n1,d_{sig},d0)=pwr \\ d_{close}&=d \mid p\_d2t(n2,d,d_{scope})=\alpha \\ \bf{\text{effect size dependent method}}& \\ d_{scope}&=d0 \mid p\_d2t(n1,d1_{sdz},d0)=pwr \\ d_{close}&=d \mid p\_d2t(n2,d,d_{scope})=\alpha \\ \end{aligned}\]


For Uri’s method, the software performs the above computation on all pairs of simulated sample sizes (n1, n2). For the effect size dependent version, the software adds a grid of \(d1_{sdz}\) values.

I show here the code that implements the core computations and not the outer loop that invokes the core on all n1, n2 pairs or n1, n2, d1.sdz triples. The code uses R’s uniroot function to solve the equations for \(d_{scope}\) and \(d_{close}\). It wraps uniroot in suppressWarnings to shut up an annoying warning from pt about possibly not attaining full precision.

## Uri's method
d.scope=uniroot(function(d0) p_d2t(n1,d=d.sig,d0,lower.tail=F)-scope.power,interval=c(0,10))$root;
d.close=suppressWarnings(uniroot(function(d) p_d2t(n2,d,d0=d.scope)-scope.close,c(-10,10))$root);

## effect size dependent method
d.scope=uniroot(function(d0) p_d2t(n1,d=d1.sdz,d0,lower.tail=F)-scope.power,interval=c(0,10))$root;
d.close=suppressWarnings(uniroot(function(d) p_d2t(n2,d,d0=d.scope)-scope.close,c(-10,10))$root);

The software stores the results of Uri’s method in a matrix scope whose row and column names are the values of n1, n2 and whose values are d.close. It stores the effect size dependent results in a data frame scopd whose columns are n1, n2, d1.sdz, and d.close.

Confidence and prediction intervals

Confidence intervals approximately indicate the range of likely values for the population effect size. Technically, the confidence level is the probability that the interval contains the population effect size.

Confidence intervals depend on the sample size and observed effect size of an individual study. The software precomputes confidence intervals on a grid of these values. The main work is done by the ci_d2t function described in the d2t document. The result is a data frame confvl whose columns are as follows.

Column names of confvl data frame

name meaning
n sample size
d.sdz standardized observed effect size
d.lo, d.hi bounds of confidence interval

Prediction intervals estimate the range of effect sizes we’re likely to observe in a replication.

Prediction intervals depend on the sample sizes of the original and replica studies and the observed effect size of the original study. The software precomputes prediction intervals on a grid of these values. The main work is done by the pi_d2t function in the d2t document. The result is a data frame predvl whose columns are as follows.

Column names of predvl data frame

name meaning
n1, n2 sample sizes of the two studies
d1.sdz standardized observed effect size of the original study
d.lo, d.hi bounds of prediction interval

Statistics computed on individual studies

The following table lists the statistics computed on individual studies. Most are obvious.

variable meaning details
mean0, mean1, sd0, sd1 means and standard deviations of group0 and group1
d.raw raw effect size mean0-mean1
sd pooled standard deviation sqrt((sd0^2+sd1^2)/2); see explanation below
d.sdz standardized effect size d.raw/sd
pval p-value computed from t-distribution d2pval(n,d.sdz); n is sample size, d2pval defined in d2t document
d.abs, d.sign absolute value and sign of d.sdz d.sign is -1 for negative, 1 for positive, 0 for the very unlikely case that d.sdz is exactly 0
ci.lo, ci.hi lower and upper bounds of confidence interval interpolated from precomputed confvl matrix
w.meta, d.meta inverse variance weight and weighted effect size for fixed effect meta-analysis see explanation below

In the current implementation, the software discards mean0, mean1, sd0, sd1, and d.raw early in the workflow, since they’re not used later. This is a minor optimization.

Pooled standard deviation

My code for pooled standard deviation, though independent of the sample size \(n\), is correct. Here’s the derivation. \[\begin{aligned} &\bf{\text{standard formula for sample sizes n0, n1}} \\{((n0-1)sd0^2+(n1-1)sd1^2)/(n0+n1-2)} \\ &\bf{\text{when n0=n1=n}} \\ &=\sqrt{((n-1)sd0^2+(n-1)sd1^2)/(n+n-2)} \\ &=\sqrt{((n-1)(sd0^2+sd1^2))/2(n-1)} \\ &=\sqrt{(sd0^2+sd1^2)/2} \\ \end{aligned}\]

Values for fixed-effect meta-analysis

The code computes the inverse variance weight and weighted effect size for fixed-effect meta-analysis on each study, then later combines these values to yield the final meta-analytic statistics for pairs of studies.

The variance in question is the sampling variance of the estimated effect size (\(d_{sdz}\)), not the variance of the data! The sampling distribution of \(d_{sdz}\) is a noncentral t-distribution centered on \(d_{sdz}\) with \(2(n-1)\) degrees of freedom. The weighted effect size is simply \(d_{sdz}\) times the weight. I use the sd_d2t function in the d2t document to get the standard deviation, then square it to get the variance.

The R code is

## sd_d2t function in Statistical Notes section

Why fixed-effect meta-analysis? With two studies it’s possible to do fixed- or random-effect but not mixed-effect which needs at least three studies. I chose fixed-effect for pragmatic performance reasons. I tested the random-effect calculation from metafor; it adds ~1 minute per replication which is way too slow for this simulation.

Statistics computed on replications

Several of the statistics computed on replications simply interpolate or directly read-out statistics precomputed before the simulation [see above]](#statistics-computed-before-simulation).

variable meaning details
pi.lo, pi.hi bounds of prediction intervals interpolated from precomputed predvl data frame
scp1, scp2 small telescope thresholds (Uri’s original version) directly read-out from precomputed scope matrix
scpd1, scpd2 effect size dependent small telescope thresholds (my extended version) interpolated from precomputed scopd data frame

The rest are meta-analytic. A key point is that the sampling distribution of the meta-effect size is normal with standard deviation \(\sqrt{1/sum\ of\ weights}\). I checked my results against metafor but don’t have a good intuition for the approach.

variable meaning details
w.sum sum of weights for the two studies
meta.d meta-effect size sum of the weighted effect sizes divided by w.sum
meta.abs absolute value of meta.d abs(meta.d) standard deviation of distribution of meta.d sqrt(1/(w.sum))
meta.pval p-value of meta.d 2*pnorm(-abs(meta.d/ distance from meta.d to bounds of confidence interval qnorm(p=0.5+conf.level/2,, bounds of meta-analytic confidence interval,

Implementation of measures

With the precursor statistics in hand, the measures are trivial. Here is the code. In this code, s1, s2 are data frames containing statistics for the two studies; p1, p2 are prediction intervals relative to the effect sizes of the two studies; and $ is R’s standard notation for accessing the columns of a data frame. between is a simple function that tests whether the first argument is between the other two: between=function(x,lo,hi) x>=lo&x<hi.

  ## apply the rules. do them in-line for efficiency
  ## use concise terms so result won't be too wide to view
  ##   d{12m} means effect size for s1, s2, meta
  ##   c{12m} means confidence interval for s1, s2, meta
  ##   scp{12} is small telescope threshold for s1, s2
  ##   scps{12} is effect size dependent small telescope threshold for s1, s2
  ##   term.term means compare the two terms, eg, d1.c2 means s1 d.sdz in s2 confidence interval
  ## significant: s1,s2,meta 
  ## same direction (ie, sign): s1,s2
  ## d in confidence interval: each vs other two
  ## confidence intervals overlap: all 3 unique pairs
  ##   thanks to
  ##   for simple calculation I should have known already :)
  c1.c2=s1$ci.lo<=s2$ci.hi & s2$ci.lo<=s1$ci.hi;$ci.lo< &<=s1$ci.hi;$ci.lo< &<=s2$ci.hi;
  ## d in prediction interval: each d vs two intervals we have
  ## prediction intervals overlap
  p1.p2=p1$pi.lo<=p2$pi.hi & p2$pi.lo<=p1$pi.hi;
  ## d >= small telescope boundary. use d.abs instead of d.sdz to handle negative values
  ## d >= effect size dependent small telescope boundary
  ##   need as.vector else R treats it as matrix. screws up naming in data.frame
  ## d2 bigger (actually more extreme) than d1
  ## return as data.frame. downstream code uses it as environment

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