Statistical Power from Pilot Data: An Example

statistical power
hypothesis tests
power analysis
In this post, I provide an example of how pilot data can be used to predict the standard error in a planned study.

Carlisle Rainey


June 10, 2024

We can think of statistical power as determined by the ratio \(\frac{\tau}{SE}\), where \(\tau\) is the treatment effect and SE is the standard error of the estimate. To reason about statistical power, one needs to make assumptions or predictions about the treatment effect and the standard error.

And as data-oriented researchers, we often want to use data to inform these predictions and assumptions. We might want to use pilot data.1

  • 1 Here’s how Leon, Davis, and Kraemer (2011) describe the purpose of a pilot study. “The fundamental purpose of conducting a pilot study is to examine the feasibility of an approach that is intended to ultimately be used in a larger scale study. This applies to all types of research studies. Here we use the randomized controlled clinical trial (RCT) for illustration. Prior to initiating a full scale RCT an investigator may choose to conduct a pilot study in order to evaluate the feasibility of recruitment, randomization, retention, assessment procedures, new methods, and/or implementation of the novel intervention. A pilot study, however, is not used for hypothesis testing. Instead it serves as an earlier-phase developmental function that will enhance the probability of success in the larger subsequent RCTs that are anticipated.”

  • Usually:

    1. Pilot data are not useful to predict the treatment effect.
    2. Pilot data are useful to predict the standard error.

    With a predicted standard error in hand, we can predict the minimum detectable effect, the statistical power, or the required sample size in the planned study.

    In this post, I give an example of how this can work.

    Predicting the SE from pilot data

    Here’s how I suggest we use pilot data to predict the standard error in the planned study:

    Predicting the SE in the planned study using pilot data

    Conservatively, the standard error will be about \(\sqrt{\frac{n^{pilot}}{n^{planned}}} \cdot \left\lbrack \left( \sqrt{\frac{1}{n^{pilot}}} + 1 \right) \cdot {\widehat{SE}}_{\widehat{\tau}}^{pilot} \right\rbrack\), where \(n^{pilot}\) is the number of respondents per condition in the pilot data, \(SE_{\widehat{\tau}}^{pilot}\) is the estimated standard error using the pilot data, and \(n^{planned}\) is the number of respondents per condition in the planned study.

    The factor \(\left( \sqrt{\frac{1}{n^{pilot}}} + 1 \right)\) nudges the standard error from the pilot study in a conservative direction, since it might be an under-estimate of the actual standard error.2 For the details, see this early paper, but this conservative standard error estimate is approximately the upper bound of a 95% confidence interval for the standard error using the pilot data.

  • 2 More generally, we can use a bootstrap to conservatively estimate the standard error, without relying on this analytical approximation.

  • Example

    The Robbins et al. study

    As an example, let’s use half of the experiment conducted by Robbins et al. (2024).

    Robbins et al. use a 2x2 factorial vignette design, randomly assigning each respondent to read one of four vignettes. The vignette describes a hypothetical covert operation ordered by the president that ends in either success or failure. Then, the vignette describes a whistleblower coming forward and describes the president’s opposition in Congress as either amplifying or ignoring the whistleblower.

    President's Opposition in Congress Outcome of Operation
    Success Failure
    Amplifies Whistleblower Vignette 1: Success & Amplify Vignette 2: Failure & Amplify
    Ignores Whistleblower Vignette 3: Success & Ignore Vignette 4: Failure & Ignore

    After the vignette, the respondent is asked whether they approve of the opposition in Congress’ actions on a seven-point Likert scale from strongly approve to strongly disapprove.

    For a simple example, let’s focus on the effect of amplifying the whistleblower when the operation succeeds. That is, let’s compare responses after Vignette 1 and Vignette 3. How much does amplifying a whistleblower increase approval when the opperation succeeds? We expect a small effect here, so we should pay careful attention to power.

    The task

    We hoped to detect an effect as small as 0.35 points on the seven-point scale and had tentatively planned on 250 respondents per condition. To test the survey instrument and data provider, we conducted a small pilot with about 75 respondents per condition. Let’s use those pilot data to check whether 250 respondents seem sufficient.

    The data

    In the {crdata} package on GitHub, you can find the the pilot data we collected leading up to the main study.

    # download the {crdata} package from github

    Now let’s load the pilot data. To focus on observations where the operation succeeds, we’re going to keep only the observations where the vignette describes a successful observation.

    # load pilot data and keep only success condition
    robbins2_pilot <- crdata::robbins_pilot |>
      subset(failure == "Success") |>
    Rows: 147
    Columns: 5
    $ cong_overall <dbl> 3, 1, -2, 0, -2, -1, 0, -1, 0, 0, 0, 2, -1, 3, -3, 0, 0, …
    $ failure      <fct> Success, Success, Success, Success, Success, Success, Suc…
    $ amplify      <fct> Ignore, Ignore, Ignore, Amplify, Ignore, Ignore, Ignore, …
    $ pid7         <fct> Strong Democrat, Not very strong Republican, Strong Democ…
    $ pid_strength <dbl> 3, 2, 3, 2, 2, 3, 2, 2, 2, 2, 2, 1, 0, 3, 3, 3, 3, 1, 1, …

    cong_overall is the respondent’s approval of Congress’ actions on a seven-point scale and amplify indicates whether Congress amplified the whistleblower (i.e., criticized the president).

    Analyzing the pilot data

    Now let’s analyze the pilot data as we plan to analyze the main data set that we plan to collect later. We’re interested in the average response in the Amplify and Ignore conditions, so let’s use a t-test.

    # t test
    fit_pilot <- t.test(cong_overall ~ amplify, data = robbins2_pilot)
    Ignore the Estimated Treatment Effect

    It can be really tempting to look at the estimated treatment effect. In this pilot study, it’s actually statistically significant. I intentionally don’t show the estimated treatment effect (or quantities requiring it, like p-values). If we looked at these, we might make one of the following mistakes:

    1. “The pilot got significant results, therefore even the pilot is sufficiently powered.”
    2. “The estimate from the pilot is significant, therefore we can use the estimated treatment effect in the power analysis.”

    Both of these claims are misleading. The estimated treatment effect is very noisy, so ignore the estimated treatment effect.

    Predicting the SE in the main study

    To predict the standard error in the main study, we need two pieces of information from this pilot:

    1. the sample size per condition and
    2. the estimated standard error.

    We can get the number of observations per condition using table().

    # create a table showing the observations per condition
     Ignore Amplify 
         70      77 
    # sample size per condition
    n_pilot <- mean(table(robbins2_pilot$amplify))
    [1] 73.5

    And then we need the estimated standard error, which is computed by t.test().

    # get estimated standard error from pilot
    se_hat_pilot <- fit_pilot$stderr
    [1] 0.2761011

    Now we can predict the standard error in the planned study.

    For the main study, we planned on about 250 respondents per condition.

    n_planned <- 250

    The we can conservatively predict the standard error in the full study as \(\sqrt{\frac{n^{pilot}}{n^{planned}}} \cdot \left\lbrack \left( \sqrt{\frac{1}{n^{pilot}}} + 1 \right) \cdot {\widehat{SE}}_{\widehat{\tau}}^{pilot} \right\rbrack\).

    pred_se_cons <- sqrt(n_pilot/n_planned)*((sqrt(1/n_pilot) + 1)*se_hat_pilot)
    [1] 0.1671691

    But is this standard error small enough?

    Evaluating the predicted SE in the main study

    We can convert the standard error to the minimum detectable effect with 80% power using \(2.5 \times SE\).3

  • 3 See Bloom (1995) for an excellent discussion of this rule. I also write about it here.

  • # compute conservative minimum detectable effect
    [1] 0.4179227

    We hoped to detect an effect as small as 0.35 points on the seven-point scale, so we’re going to need more than 250 respondents per condition!

    We can also compute the power to detect an effect of 0.35 points on the seven-point scale.

    # compute power as a percent
    1 - pnorm(1.64 - 0.35/pred_se_cons)
    [1] 0.6749736

    Note that these are conservative estimates of the minimum detectable effect and statistical power.

    Here’s what things look like if we remove the conservative nudge \(\left( \sqrt{\frac{1}{n^{pilot}}} + 1 \right)\) and predict the standard error as \(\sqrt{\frac{n^{pilot}}{n^{planned}}} \cdot {\widehat{SE}}_{\widehat{\tau}}^{pilot}\).

    # without the conservative nudge
    pred_se <- sqrt(n_pilot/n_planned)*se_hat_pilot
    2.5*pred_se  # best guess of minimum detectable effect
    [1] 0.3742673
    1 - pnorm(1.64 - 0.35/pred_se)  # best guess of power
    [1] 0.7573806

    As you can see, the minimum detectable effect and power are a little too low. We need more respondents!

    Adjusting the Sample Size

    Our plan of 250 respondents per condition seems too low. If we want, we can predict the sample size we need to get to 80% power using the following rule:

    Predicting the required sample size in the planned study using pilot data

    For 80% power to detect the treatment effect \(\widetilde{\tau}\), we will (conservatively) need about \(n^{pilot} \cdot \left\lbrack \frac{2.5}{\widetilde{\tau}} \cdot \left( \sqrt{\frac{1}{n^{pilot}}} + 1 \right) \cdot {\widehat{SE}}_{\widehat{\tau}}^{pilot} \right\rbrack^{2}\) respondents per condition, where \(n^{pilot}\) is the number of respondents per condition in the pilot data and \(SE_{\widehat{\tau}}^{pilot}\) is the estimated standard error using the pilot data.

    n_pilot*((2.5/0.35)*((sqrt(1/n_pilot) + 1)*se_hat_pilot))^2
    [1] 356.4477

    Thus to get 80% power, the pilot data suggest that we (conservatively) need about 360 respondents per condition. We used 367 in the full study. Here are the conservative predictions for 367 respondents per condition.

    n_planned <- 367
    pred_se_cons <- sqrt(n_pilot/n_planned)*((sqrt(1/n_pilot) + 1)*se_hat_pilot)
    [1] 0.1379726
    2.5*pred_se_cons  # conservative minimum detectable effect
    [1] 0.3449315
    1 - pnorm(1.64 - 0.35/pred_se_cons)  # conservative power
    [1] 0.8150699

    How did we do?

    We ran the full study.4

  • 4 See Robbins et al. (2024) for the full results.

  • robbins2_main <- crdata::robbins_main |>
      subset(failure == "Success") |>
    Rows: 735
    Columns: 5
    $ cong_overall <dbl> 2, -2, -1, -3, 0, -1, -2, -1, 1, 1, 0, -3, -2, 2, 2, -3, …
    $ failure      <fct> Success, Success, Success, Success, Success, Success, Suc…
    $ amplify      <fct> Ignore, Amplify, Amplify, Amplify, Ignore, Ignore, Amplif…
    $ pid7         <fct> Not very strong Republican, Not very strong Republican, S…
    $ pid_strength <dbl> 2, 2, 3, 3, 3, 2, 0, 2, 2, 1, 2, 0, 3, 1, 2, 3, 2, 3, 2, …
    fit_main <- t.test(cong_overall ~ amplify, data = robbins2_main)
    [1] 0.1322618
    1 - pnorm(1.64 - 0.35/fit_main$stderr)
    [1] 0.8428563

    As you can see, the pilot data gave us a good, slightly conservative prediction. We conservatively predicted a standard error of 0.138 in the planned study and we estimated a standard error of 0.132 after running the study. We conservatively predicted our power would be about 82% to detected an effect of 0.35 on the seven-point scale, but after running the study, it seems like we had about 84% power.

    A bootstrap alternative

    We can also use the bootstrap as an alternative. There are a few ways one might approach it.

    Here’s one:

    1. Treat the pilot data as a population. Create a data set with the planned sample size by sampling with replacement from the pilot data.
    2. Perform the planned analysis on each resampled data set.
    3. Store the estimated standard error from each analysis.

    Repeat the process above many times. For each standard error estimate, compute the implied statistical power. This gives a distribution of power estimates. Find a value near the bottom of this distribution. The factor we used above—The factor \(\left( \sqrt{\frac{1}{n^{pilot}}} + 1 \right)\)—nudges the standard error to about the 2.5th percentile, so we can use that here, too.

    # number of bootstrap iterations
    n_bs <- 10000
    bs_se <- numeric(n_bs)  # a container
    for (i in 1:n_bs){
      # resample 367 observations from each condition
      bs_data <- robbins2_main %>%
        group_by(amplify) %>%
        sample_n(size = 367, replace = TRUE)
      # run planned analysis
      bs_fit <- t.test(cong_overall ~ amplify, data = bs_data)
      # grab se
      bs_se[i] <- bs_fit$stderr
    # compute 2.5th percentile of power to obtain conservative estimate
    pwr <- 1 - pnorm(1.64 - 0.35/bs_se)
    quantile(pwr, probs = 0.025)

    Using the analytical approximation, we got 0.815 as a conservative estimate of power. The bootstrap gave us 0.820 as a conservative estimate. The actual power in the full study turned out to be about 0.843. (Remember, all of these power calculations are power to detected an effect of 0.35 points on the seven-point scale.)

    The paper

    I have an early draft of a paper on these (and other) ideas. Please test them out in your own work and let me know if you have questions, comments, and suggestions. I’m interested in making the paper as clear and useful as I can.


    Bloom, Howard S. 1995. “Minimum Detectable Effects.” Evaluation Review 19 (5): 547–56.
    Leon, Andrew C., Lori L. Davis, and Helena C. Kraemer. 2011. “The Role and Interpretation of Pilot Studies in Clinical Research.” Journal of Psychiatric Research 45 (5): 626–29.
    Robbins, Caroline, Alessandro Brunelli, Jose Casto, Ainsley Coty, Andrew Louis, Bryanna Major, Maria A Martinez, et al. 2024. “Overt Consequences of Covert Actions: Success, Failure, and Voters’ Preferences for Legislative Oversight.” SocArXiv.