12.6 Shuffle, Resample, and Standard Error

We started with the idea that we could slide the sampling distribution of \(b_1\) up and down to find the lower and upper bounds of the confidence interval. Noticing that the center of this confidence interval was right at the sample \(b_1\), we used resample() to bootstrap a sampling distribution that would be centered at the sample \(b_1\). This helped us calculate the upper and lower bounds.

To do all this, however, we assumed that the sampling distributions generated from different DGPs (e.g., different values of \(\beta_1\) such as 0.00, 6.05, 13.00, etc.) would all have the same shape and spread. We have now used two methods to create sampling distributions of \(b_1\), each based on a different DGP. Do these sampling distributions have the same shape and spread?

Using shuffle(), we simulated a DGP in which the \(\beta_1=0\) (that is, the empty model is true). It’s depicted in the left panel of the figure below. Using resample() (right panel of the figure), we simulated a DGP in which the true value of \(\beta_1=6.05\), the same as the sample \(b_1\).

Although the centers of the two sampling distributions are different, the shapes of the two distributions are similar. Both are roughly normal and symmetrical. Though the one produced by resample() appears to be somewhat asymmetrical – it’s skewed a little to the right – we will, for now, consider it close enough to symmetrical.

The Importance of Standard Error

The most important feature of the sampling distributions, however, is their width. We can eyeball the range from the histograms above (e.g., both are around 20) and already see that they are similar. A more commonly used measure of spread is standard error. In the code window below, use favstats() to calculate the standard errors of the two sampling distributions: made from shuffle and resample. (We have included code to create the two sampling distributions for you.)

In the first output below we show the favstats for the \(b_1\)s created by shuffle(). In the second, we show the favstats created by resample().

       Min       Q1      median       Q3      max        mean       sd    n missing
 -9.954545     -2.5 -0.04545455      2.5 10.22727 -0.03554545 3.498973 1000       0

       min       Q1      median       Q3      max        mean       sd    n missing
 -3.219048 3.772727    5.921166 8.480083 15.96154    6.110566 3.381418 1000       0

The favstats reveal that the means of the two sampling distributions are roughly as expected: the shuffled distribution has a mean that is fairly close to 0, and the resampled distribution has a mean close to the sample \(b_1\) of 6.05.

While the means are different (0 versus 6.05), the standard deviations of the two distributions are quite similar to each other: 3.50 for the shuffled distribution, and 3.38 for the resampled distribution. Because these are standard deviations of sampling distributions we call them standard errors.

The fact that the standard errors are similar is an important feature of sampling distributions. The constancy of standard error, along with the shape, is what allows us to assume that we can slide sampling distributions up and down the x-axis when we are constructing a confidence interval.

A larger standard error means that the spread of the sampling distribution is larger, which in turn means there is more variability (or uncertainty) in our estimate. If there is more variability in the estimate, we should be less confident that our best estimate is a reflection of the true parameter.

A Mathematical Formula for Standard Error

When R models a sampling distribution as a t-distribution, it makes its own calculation of what the standard error is. It does this based on a formula, developed by mathematicians, which is part of something called the Central Limit Theorem.

The Central Limit Theorem provides a way of finding the standard error of a sampling distribution based on the estimated variance of the outcome variable. For the sampling distribution of \(b_1\), when \(b_1\) is the difference between two groups, the standard error can be estimated with this formula:

\[\sqrt{\frac{s_1^2}{n_1} + \frac{s_2^2}{n_2}}\]

The \(s_1\) in this formula is the variance on the outcome variable (Tip) for group one, which in our example would be the Control group. \(n_1\) is the sample size for the control group. Similarly, the Smiley Face group would be \(s_2\) and \(n_2\).

Don’t worry, you won’t need to calculate this yourself. We just want you to know what R is doing when it uses a t-distribution. It doesn’t shuffle or bootstrap a sampling distribution and then calculate the standard deviation of the sampling distribution. It just uses this formula.

We can use the following code (no need to remember it) to fit the Condition model of Tip (you’ve done this many times by now), and then to produce the estimates and standard errors for the \(b_0\) and \(b_1\) parameter estimates.

model <- lm(Tip ~ Condition, data = TipExperiment)
summary(model)$coef

                      Estimate Std. Error   t value     Pr(>|t|)
(Intercept)          27.000000   2.351419 11.482428 1.546877e-14
ConditionSmiley Face  6.045455   3.325409  1.817958 7.620787e-02

The \(b_1\) estimate is in the second row of the column that says Estimate. As expected, it is 6.05. The standard error of the estimate (which is another way of saying the standard deviation of the sampling distribution) is 3.33.

We now have three different estimates of the standard error of the sampling distribution of \(b_1\): 3.50, 3.38, and 3.33 (from shuffling, resampling, and the mathematical equation, respectively). The important thing to note is that they are all fairly close to one another.