I was recently asked the following question:

I am using bootstrap simulations to compute critical values for a statistical test. Suppose I have test statistic for which I want a p-value. How do I compute this?

The answer to this question doesn't require knowing anything about bootstrap methods. An equivalent formulation (for a one-sided *p*-value) is "How do I count the number of values in a vector that are greater than a given value?" For *p*-values,
you assume that the vector contains random values sampled from the null distribution.

You can find a fully-worked example of a bootstrap computation for a paired *t* test on pages 11–14 of my SAS Global Forum paper, "Rediscovering SAS/IML Software:
Modern Data Analysis for the Practicing Statistician." The empirical *p*-value is computed on page 14.

Here's one way think about this problem. Suppose a vector, `s`, contains random values from the null distribution. In a bootstrap situation, this means that *s _{1}*,

*s*, ...,

_{2}*s*are the bootstrapped statistics, where

_{N}*s*is the statistic computed on the

_{i}*i*th bootstrap sample, and where each bootstrap sample is sampled from the null distribution (that is, according to the null hypothesis). Let

*s*be the value of the test statistic. Then a one-sided empirical

_{0}*p*-value for

*s*is computed as follows:

_{0}- The simplest computation is to apply the definition of a
*p*-value. To do this, count the number of values (statistics) that are greater than or equal to the observed value, and divide by the number of values. In code,`pval = sum(s >= s0)/N;` -
The previous formula has a bias due to finite sampling. Some authors suggest the modification
`pval = (1+sum(s >= s0))/(N+1);`For example, see Davison and Hinkley (1997),*Bootstrap Methods and their Application*, p. 141. Obviously, the two formulas are essentially the same when the number of values, N, is large.

See also my article on computing empirical estimates from the data.

Incidentally, if you'd like to run the bootstrap computations yourself, you can download the airlines data that I used in my SAS Global Forum paper.

## 22 Comments

i want to find Bootstrap-p and Bootstrap -t.

so i want a programme to find that values.

Thank you.

My new book

Simulating Data with SAScontains a chapter on how to use SAS to compute bootstrap distributions. You can also see the paper by David Cassell.I think the intent of the question is how to obtain p-values for a test when we have the original data (in which we don't know whether the effect is null or not), and do not already have data from the null distribution. Could you modify the bootstrap procedure to get p-values that are consistent with the 95% CIs from the bootstrap? (Eg. if the CI straddles 0 the p-value is >0.05.) Randomly permuting the treatment labels works for p-values but doesn't give CIs.

You ask two questions. The first concerns hypothesis tests. The second concerns CIs.

1) I am not assuming that the data follows the null distribution. To apply the bootstrap,you have to choose a resampling scheme. When testing a hypothesis, you should resample AS IF the hypothesis is true. You can use the resulting bootstrap distribution to estimate the p-value.

2) Yes, if you have a parameter estimate from the data you can use a bootstrap technique to form an approximate CI for the parameter. Unfortunately, the technique might depend on the estimate. For example, bootstrapping a median requires a different technique than bootstrapping a mean.

i was wondering how many instances of the null hypothesis i would need for a pvalue of say 0.05.

i could for instance only compute 10 instances of my null hypthesis and apply the above formula (pval = sum(s >= s0)/N). It is maybe 0. So, how many instance (N) do I need for a 0.05 or 0.01 pvalue?

Thanks a lot!

That is an excellent question. Unfortunately, there is no simple answer. If the sampling distribution is normal, you might only need hundreds of simulations; if the distribution is severely skewed or long-tailed, more are needed. In my book

Simulating Data with SASI discuss this problem and make several additional recommendations.respected sir can you send me this book Simulating Data with SAS. i cant purchase it. please send me the bootstrap simulation topic.

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Thanks a lot for this post. just to clarify, the formula you have provided: pval = sum(s >= s0)/N is for a 2-sided p-value and if I wanted a one-sided p-value I would instead use pval = sum(s > s0)/N or pval = sum(s < s0)/N?

Yes. Some people use "=", but when N is large it doesn't matter.

Hi Rick,

I am trying to calculate the p-values for all the values of my original sample(m=53) .My bootstrapped estimates are (N=)5000. for that I am writing the following code :

data Ave; set stacked (keep= Mean); run;

%let Mydata1=Ave; * Contain Bootstrapped estimate .Dim is 5000x1.Variable name is Mean;

%let MyData2 = pcm; * Contain actual sample.Dim is 53x1. Variable name is pcm;

proc iml;

use &Mydata1; read all var {Mean} into y; close &MyData1;

use &MyData2; read all var {pcm} into x; close &MyData2;

print x; * Just to see;

N=nrow(y) ;

m=nrow(x);

print m N;

P=j(m, 1,1); *mx1 vector of 1s;

Do i = 1 to m ;

a=x[i,1] ; print a;

idx = loc(y> a); print idx; p = nrow(idx`); * how many?;

print p N; p_val= p/N; print p_val;

P[i]= p_val ;

end;

quit;

after execution I am getting

ERROR: Matrix idx has not been set to a value.

Can you help me to fix the error?

Kaushik

You can ask programming questions at the SAS/IML Support Community.

You need to calculate the statistic of interest for the actual data and compare it to the collection of statisics from each bootstrap sample.

ok & thanks .

I will do that .

However I still think I should ask you here beacuse it contains& related to the DO Loop(my be I am wrong): The point is , as I understand from your blogs & books that the LOC function is dynamic in nature and there is no need to initialize it.It is working fine idx= loc(y>8) say.When I am putting that into a loop i.e. idx=loc(y>x[i,1]) , it is giving error message : Matrix idx has not been set to a value.

Can you give any clue as to where I am wrong?

Yes. If the condition inside the LOC function is not satisfied, then the LOC function returns an empty matrix. When you try to use idx as a subscript, the error occurs. You can read about this situation and how to handle it correctly in the article "Beware the naked LOC."

Thanks Rick. I am able to figure out now. Though I have been following your blogs last couple of years this is the first time I have interacted with you. I have learnt many a things from your blogs : not about SAS only sometimes about statistics too. Thanks again

What if the observed beta could be bigger or smaller than the bootstrap value. How would we calculate a 2 sided p value?

The computation doesn't change. For the first definition, the bootstrap estimate of the probability can be 0. You should display the estimate by using the PVALUE format, such as

print pval[format=pvalue6.];

which will display the text "<.0001".

For the second estimate, the p-value will always be nonzero.

how to use bootstrap methods for improved P values ???

How can I calculate Confident intervals from these bootstrap samples?

A discussion and example of a basic CI is in the article "Compute a bootstrap confidence interval in SAS". For more information about bootstrapping, see "The essential guide to bootstrapping in SAS."

I have a question. I computed nonparametric bootstrap hazard ratios for the variable X (X=0 vs. X=1) using 250 replications (proc surveyselect), which are given below:

CI95_lower CI95_median CI95_upper

0.66051 0.90034 1.23374

There seems to be no difference in rates of the investigated endpoint as a function of X. When I try to calculate the p-value for 1 being included (no difference between X=0 and X=1) in the bootstrap confidence interval, I get the p-values below:

N lt1 gt1

250 0.692 0.308

Here I would choose P=0.31 for a protective effect of X on the investigated endpoint (hazard ratio <1 for X=1 vs. X=0). But what if I had no knowledge about the direction of the effect? How can I know, which of the one-sided p-values I should use?

Thanks!

Yes, it can be confusing because there are so many symmetries. For example, do you model the event as X=1 or as X=0? Are you interested in the ratio P(Y|X=0)/P(Y|X=1) or the inverse ratio? In practice, we are guided by domains-specific knowledge such as assuming that the experimental treatment will have a certain effect compared to the control group. That domain knowledge helps you to write down the null and alternative hypotheses.