If you have access to data on an entire population, say the opinion of every adult in the United States on whether or not they think climate change is affecting their local community, it’s straightforward to answer questions like, “What percent of US adults think climate change is affecting their local community?”. Similarly, if you had demographic information on the population you could examine how, if at all, this opinion varies among young and old adults and adults with different leanings. If you have access to only a sample of the population, as is often the case, the task becomes more complicated. What is your best guess for this proportion if you only have data from a small sample of adults? This type of situation requires that you use your sample to make inference on what your population looks like.

Setting a seed: You will take random samples and build sampling distributions in this lab, which means you should set a seed on top of your lab. If this concept is new to you, review the lab on probability.

Getting Started

Load packages

In this lab, we will explore and visualize the data using the tidyverse suite of packages, and perform statistical inference using infer.

Let’s load the packages.

library(tidyverse)
library(openintro)
library(infer)
set.seed(02810)

The data

A 2019 Pew Research report states the following:

To keep our computation simple, we will assume a total population size of 100,000 (even though that’s smaller than the population size of all US adults).

Roughly six-in-ten U.S. adults (62%) say climate change is currently affecting their local community either a great deal or some, according to a new Pew Research Center survey.

Source: Most Americans say climate change impacts their community, but effects vary by region

In this lab, you will assume this 62% is a true population proportion and learn about how sample proportions can vary from sample to sample by taking smaller samples from the population. We will first create our population assuming a population size of 100,000. This means 62,000 (62%) of the adult population think climate change impacts their community, and the remaining 38,000 does not think so.

us_adults <- tibble(
  climate_change_affects = c(rep("Yes", 62000), rep("No", 38000))
)

The name of the data frame is us_adults and the name of the variable that contains responses to the question “Do you think climate change is affecting your local community?” is climate_change_affects.

We can quickly visualize the distribution of these responses using a bar plot.

ggplot(us_adults, aes(x = climate_change_affects)) +
  geom_bar() +
  labs(
    x = "", y = "",
    title = "Do you think climate change is affecting your local community?"
  ) +
  coord_flip()

We can also obtain summary statistics to confirm we constructed the data frame correctly.

us_adults %>%
  count(climate_change_affects) %>%
  mutate(p = n /sum(n))
## # A tibble: 2 × 3
##   climate_change_affects     n     p
##   <chr>                  <int> <dbl>
## 1 No                     38000  0.38
## 2 Yes                    62000  0.62

In this lab, you’ll start with a simple random sample of size 60 from the population.

n <- 60
samp <- us_adults %>%
  sample_n(size = n)
  1. What percent of the adults in your sample think climate change affects their local community? Hint: Just like we did with the population, we can calculate the proportion of those in this sample who think climate change affects their local community.

Answer:

60% of my sample believes climate change impacts their community.

samp %>%
  count(climate_change_affects) %>%
  mutate(p = n / sum(n))
## # A tibble: 2 × 3
##   climate_change_affects     n     p
##   <chr>                  <int> <dbl>
## 1 No                        26 0.433
## 2 Yes                       34 0.567
  1. Would you expect another student’s sample proportion to be identical to yours? Would you expect it to be similar? Why or why not?

Answer: Similar to my answer for a similar answer in Lab 5a, I would expect some deviations in the sample proportions between individual students, with random chance causing the exact same proportions. There’s a much smaller chance that the underlying sample members are the exact same. Across enough student sample attempts, I would expect at least one matching pair.

Confidence intervals

Return for a moment to the question that first motivated this lab: based on this sample, what can you infer about the population? With just one sample, the best estimate of the proportion of US adults who think climate change affects their local community would be the sample proportion, usually denoted as \(\hat{p}\) (here we are calling it p_hat). That serves as a good point estimate, but it would be useful to also communicate how uncertain you are of that estimate. This uncertainty can be quantified using a confidence interval.

One way of calculating a confidence interval for a population proportion is based on the Central Limit Theorem, as \(\hat{p} \pm z^\star SE_{\hat{p}}\) is, or more precisely, as \[ \hat{p} \pm z^\star \sqrt{ \frac{\hat{p} (1-\hat{p})}{n} } \]

Another way is using simulation, or to be more specific, using bootstrapping. The term bootstrapping comes from the phrase “pulling oneself up by one’s bootstraps”, which is a metaphor for accomplishing an impossible task without any outside help. In this case the impossible task is estimating a population parameter (the unknown population proportion), and we’ll accomplish it using data from only the given sample. Note that this notion of saying something about a population parameter using only information from an observed sample is the crux of statistical inference, it is not limited to bootstrapping.

In essence, bootstrapping assumes that there are more of observations in the populations like the ones in the observed sample. So we “reconstruct” the population by resampling from our sample, with replacement. The bootstrapping scheme is as follows:

  • Step 1. Take a bootstrap sample - a random sample taken with replacement from the original sample, of the same size as the original sample.
  • Step 2. Calculate the bootstrap statistic - a statistic such as mean, median, proportion, slope, etc. computed on the bootstrap samples.
  • Step 3. Repeat steps (1) and (2) many times to create a bootstrap distribution - a distribution of bootstrap statistics.
  • Step 4. Calculate the bounds of the XX% confidence interval as the middle XX% j knof the bootstrap distribution.

Instead of coding up each of these steps, we will construct confidence intervals using the infer package.

Below is an overview of the functions we will use to construct this confidence interval:

Function Purpose
specify Identify your variable of interest
generate The number of samples you want to generate
calculate The sample statistic you want to do inference with, or you can also think of this as the population parameter you want to do inference for
get_ci Find the confidence interval

This code will find the 95 percent confidence interval for proportion of US adults who think climate change affects their local community.

samp %>%
  specify(response = climate_change_affects, success = "Yes") %>%
  generate(reps = 1000, type = "bootstrap") %>%
  calculate(stat = "prop") %>%
  get_ci(level = 0.95)
## # A tibble: 1 × 2
##   lower_ci upper_ci
##      <dbl>    <dbl>
## 1     0.45    0.683
  • In specify we specify the response variable and the level of that variable we are calling a success.
  • In generate we provide the number of resamples we want from the population in the reps argument (this should be a reasonably large number) as well as the type of resampling we want to do, which is "bootstrap" in the case of constructing a confidence interval.
  • Then, we calculate the sample statistic of interest for each of these resamples, which is proportion.

Feel free to test out the rest of the arguments for these functions, since these commands will be used together to calculate confidence intervals and solve inference problems for the rest of the semester. But we will also walk you through more examples in future chapters.

To recap: even though we don’t know what the full population looks like, we’re 95% confident that the true proportion of US adults who think climate change affects their local community is between the two bounds reported as result of this pipeline.

Confidence levels

  1. In the interpretation above, we used the phrase “95% confident”. What does “95% confidence” mean?

Answer: 95% confident means that, repeated enough times, 95% of the calculated intervals would capture the real and true proportion of the population that believes climate change is real.The inverse here is that, 5% of the time, the interval would not capture it and potentially be wildly wrong. The 95 confident is about overall reliability across a larger set of samples. Confidence intervals are about a range of plausible values and the 95% confident reflects a probability on that interval.

In this case, you have the rare luxury of knowing the true population proportion (62%) since you have data on the entire population.

  1. Does your confidence interval capture the true population proportion of US adults who think climate change affects their local community? If you are working on this lab in a classroom, does your neighbor’s interval capture this value?

Answer: I am answering this question alone and will only address the first question.My 95% confidence interval is 0.467 to 0.717. Technically, .62 is within this range but the range is also 25%. If I showed you a presidential poll that said Kamala Harris has 48%, with a 95% confidence range of 35% to 60%, would you treat it as a valuable piece of data to use in decision making? In the real world, these confidence intervals need to be significantly lower, closer to +-3%, to be useful.

  1. Each student should have gotten a slightly different confidence interval. What proportion of those intervals would you expect to capture the true population mean? Why?

Answer: I would expect, assuming enough student samples were taken, 95% of the intervals to capture the 62% actual value and 5% of them to not.

In the next part of the lab, you will collect many samples to learn more about how sample proportions and confidence intervals constructed based on those samples vary from one sample to another.

  • Obtain a random sample.
  • Calculate the sample proportion, and use these to calculate and store the lower and upper bounds of the confidence intervals.
  • Repeat these steps 50 times.

Doing this would require learning programming concepts like iteration so that you can automate repeating running the code you’ve developed so far many times to obtain many (50) confidence intervals. In order to keep the programming simpler, we are providing the interactive app below that basically does this for you and created a plot similar to Figure 5.6 on OpenIntro Statistics, 4th Edition (page 182).

  1. Given a sample size of 60, 1000 bootstrap samples for each interval, and 50 confidence intervals constructed (the default values for the above app), what proportion of your confidence intervals include the true population proportion? Is this proportion exactly equal to the confidence level? If not, explain why. Make sure to include your plot in your answer.

Answer:

2 out of 50, or 96%, produced intervals that captured the true value. This is one percentage point higher than the confidence interval of 95%.

95% bootstrap chart
95% bootstrap chart

More Practice

  1. Choose a different confidence level than 95%. Would you expect a confidence interval at this level to be wider or narrower than the confidence interval you calculated at the 95% confidence level? Explain your reasoning.

Answer: I chose a 90% confidence interval and expected more samples to fall outside of the confidence interval. When I ran it, I got exactly what I expected. The data went from 96% in the interval at 95% confidence to 94% in the interval at 90% confidence. I expected this because a lower confidence level results in a narrower confidence interval, creating a narrower range where the true value can fall and still be in it.

  1. Using code from the infer package and data from the one sample you have (samp), find a confidence interval for the proportion of US Adults who think climate change is affecting their local community with a confidence level of your choosing (other than 95%) and interpret it.

Answer:

I chose 90% confidence level and got a confidence interval of 0.483 to 0.7. The interval did narrow a bit, down to 21.7% versus closer to 25% at 95%.

samp %>%
  specify(response = climate_change_affects, success = "Yes") %>%
  generate(reps = 1000, type = "bootstrap") %>%
  calculate(stat = "prop") %>%
  get_ci(level = 0.90)
## # A tibble: 1 × 2
##   lower_ci upper_ci
##      <dbl>    <dbl>
## 1     0.45    0.667
  1. Using the app, calculate 50 confidence intervals at the confidence level you chose in the previous question, and plot all intervals on one plot, and calculate the proportion of intervals that include the true population proportion. How does this percentage compare to the confidence level selected for the intervals?

Answer: Of the 50 intervals bootstrap resampled 1,000 times at 90% confidence level, 5 out of 50, or 10%, had confidence intervals that did not capture the population value. This is an exact match for the 90% confidence interval, where I would expect, over a large enough number of samples of the population, 10% of the intervals to not capture the true population calue.

90% bootstrap chart
90% bootstrap chart
  1. Lastly, try one more (different) confidence level. First, state how you expect the width of this interval to compare to previous ones you calculated. Then, calculate the bounds of the interval using the infer package and data from samp and interpret it. Finally, use the app to generate many intervals and calculate the proportion of intervals that are capture the true population proportion.

Answer:

I chose a 75% confidence interval because I expected to see a much smaller range that doesn’t catch the population value enough times consistently to produce reliable statistics. The confidence interval I got was .517 to .683, which does capture the population value of .62.

samp %>%
  specify(response = climate_change_affects, success = "Yes") %>%
  generate(reps = 1000, type = "bootstrap") %>%
  calculate(stat = "prop") %>%
  get_ci(level = 0.75)
## # A tibble: 1 × 2
##   lower_ci upper_ci
##      <dbl>    <dbl>
## 1    0.483    0.633

When I ran the simulation app using settings as the previous runs except for changing the confidence interval, 16 out of 50, or 32%, of the confidence intervals did not capture the population value. At 75% confidence, I would expect this value to be 25% so this is 8 percentage points higher.

  1. Using the app, experiment with different sample sizes and comment on how the widths of intervals change as sample size changes (increases and decreases).

Answer: A sample size of 400 at 95% confidence produced 2 out of 50 no capturing the population value, which is the same as sample size of 60. The confidence intervals got tighter, with most clustering between .55 and .7. At sample size of 60, this stretched out to cover .4 to .8

Increasing the sample size to 1,000 while reducing the confidence level to 90% lead to confidence intervals clustered between .6 and .66.

Overall, The sweetspot for this dataset is a sample around 800 to 1,000 and a 95% confidence level. For these surveys where you have all the answers and are just picking the optimal sample from data you already have, reaching for the most useable stastistics is a good approach. However, if you’re trying to figure out how many peeople to survey in a population for your results to matter, I would not use this approach.

  1. Finally, given a sample size (say, 60), how does the width of the interval change as you increase the number of bootstrap samples. Hint: Does changing the number of bootstap samples affect the standard error?

Answer: I changed your app code for this one so I could smash in the SEs and average across them into the UI. The law of dimnishing returns started to take over as I increased the number of bootstrap samples whiles keeping the sample size at 60 and the confidence interval at 95%

bootstrap samples: 800 Average SE: .0628

Boostrap samples: 1000 Average SE: .0627

Boostrap samples: 1400 Average SE: .0621

Boostrap samples: 1800 Average SE: .062

Increasing the sample size to 200 under the same other parameters lead to the biggest change, with the average standard error dropping to .0345