Linked Questions

388
votes
11answers
166k views

Explaining to laypeople why bootstrapping works

I recently used bootstrapping to estimate confidence intervals for a project. Someone who doesn't know much about statistics recently asked me to explain why bootstrapping works, i.e., why is it that ...
47
votes
3answers
38k views

Bootstrap vs. permutation hypothesis testing

There are several popular resampling techniques, which are often used in practice, such as bootstrapping, permutation test, jackknife, etc. There are numerous articles & books discuss these ...
46
votes
1answer
44k views

Alternatives to one-way ANOVA for heteroskedastic data

I have data from 3 groups of algae biomass ($A$, $B$, $C$) which contain unequal sample sizes ($n_A=15$, $n_B=13$, $n_C=12$) and I would like compare if these groups are from the same population. One-...
23
votes
1answer
23k views

Using bootstrap under H0 to perform a test for the difference of two means: replacement within the groups or within the pooled sample

Suppose that I have a data with two independent groups: ...
9
votes
1answer
25k views

Why would I want to bootstrap when computing an independent sample t-test? (how to justify, interpret, and report a bootstrapped t-test)

Let's say I have two conditions, and my sample size for the two conditions is extremely low. Let's say I only have 14 observations in the first condition and 11 in the other. I want to use the t-test ...
18
votes
2answers
28k views

Is Random Forest suitable for very small data sets?

I have data set comprising 24 rows of monthly data. The features are GDP, airport arrivals, month, and a few others. The dependent variable is number of visitors to a popular tourism destination. ...
16
votes
3answers
4k views

Bootstrap: the issue of overfitting

Suppose one performs the so-called non-parametric bootstrap by drawing $B$ samples of size $n$ each from the original $n$ observations with replacement. I believe this procedure is equivalent to ...
6
votes
4answers
2k views

How to calculate the mean and CI for a percentage

I have some data that is percent reduction, i.e., -%, from a study and I want to be able to summarize the average percent reduction from my n = 20 samples, and to also provide a confidence interval. I ...
10
votes
4answers
802 views

Why do hypothesis tests on resampled datasets reject the null too often?

tl;dr: Starting with a dataset generated under the null, I resampled cases with replacement and conducted a hypothesis test on each resampled dataset. These hypothesis tests reject the null more than ...
2
votes
2answers
1k views

Bootstrapping and hypothesis testing

I got a comment on a paper that I recently submitted. He said, "Pag 7: referring to the “Univariate Analysis” section, bootstrap is not mentioned. This technique is extremely useful when dealing ...
6
votes
3answers
688 views

Estimating the ratio of cell means in ANOVA under lognormal assumption

I am conducting a two-sample test (1-way ANOVA with 2 treatments), and the goal is to estimate the ratio of cell means assuming that the data are lognormal. A simple approach is to log the response ...
1
vote
2answers
363 views

Is it erroneous to double data for a larger sample size?

Is it a big no no to double data in order to increase sample size? I have a sample size of 27 which really doesn't give me much to work with when running tests. Would this be statistically incorrect? ...
0
votes
1answer
581 views

Log transform before bootstrapping?

Background: I’m exploring if hair cortisol levels (pg/mg) are related with executive functions like working memory (measured on WISC-scale), inhibition control (BRIEF-scale) and attention shifting (...
2
votes
1answer
290 views

Test to show one distribution is bigger than another

Here is a MWE of my problem: I measure the size, $S$, of 10 red apples and 32 green apples. $\bar S_\mathrm{red} = 8 \pm 1\,\mathrm{cm}$ and $\bar S_\mathrm{green} = 4 \pm 2\,\mathrm{cm}$. I want ...
14
votes
0answers
203 views

Is bootstrap problematic in small samples?

In "3 Things That Bother Me" (1988), Ed Leamer writes: Bootstrap estimates of standard errors are based on the assumption that the observed sample is the same as the true distribution, ...

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