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(4321)

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))

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.

Insert your answer here

samp %>%
  count(climate_change_affects) %>%
  mutate(p = n/sum(n))

Ans: 63% of adults in the sample population thinks that climate change affects their local community.

  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?

Insert your answer here Ans: I would not expect another student’s sample to be identical to mine. However, it will be similar because the samples are randomly selected from the population and variation due to randomness can contribute to the slight differences in sample results.

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:

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)

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?

Insert your answer here

ANS: 95% confidence means that if we were to take many random samples from the population and calculate the confidence interval for each sample, we are certain that 95% of the time these intervals will capture the true value. In the above case, we are reasonably certain the interval 50% to 75% captures the true population in this sample population. Although there is a chance (eg. 5%) that the true population can lie outside of the interval, we are confident in the method that we used to make such estimation.

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?

Insert your answer here

Ans: The confidence interval is 50% to 75%. Therefore, the does capture the true population, which is 62%.

  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?

Insert your answer here

Ans: 95% of those intervals will capture the true population because this method is calculated based on the the CI 95%, which is Z-score=1.96

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.

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.

Insert your answer here

Ans: 96% of the intervals captured the true value, and two intervals fell outside. This proportion does not exactly equal to the 95% confidence level because of randomness and variability in the sampling process.

plot
plot

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.

    Insert your answer here

    Ans: I pick CI = 99%. It will create a wider Ci than 95% because in order to be certain to capture the true value, wider intervals are needed. Mathematically, higher Ci creates a wider spread (SE* Z-score).

  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.

Insert your answer here

Ans: The Ci is from 48% to 78% at 99% CI. In other words, there are about 48% to 78% US adults believe that climate change affects their local community and this estimation is going to capture the true value about 99% of the time. The interval is much wider than 95% because it needs to be accurate and thus a wider range is needed.

samp %>%
  specify(response = climate_change_affects, success = 'Yes') %>%
  generate(rep=5000, type = 'bootstrap') %>%
  calculate(stat='prop') %>%
  get_ci(level = 0.99)
  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?

Insert your answer here

Ans: 49 out of 50 intervals captured the true population, and that is about 98%. This shows the method that we used fairly accurate however the sample size(number of intervals) might be too small to get within the confidence level. Secondly, empirical data can vary from theoretical calculation such as the simulation and thus we would observe a fairly close approixmation rather than a perfectly matched results.

plot2
plot2
  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.

Insert your answer here

Ans: Ci= 90. The interval range will be narrower compared to the previous Cis. The Ci is from 53% to 73% at 90% CI. In other words, there are about 53% to 73% US adults believe that climate change affects their local community and this estimation is going to capture the true value about 90% of the time. Using the App, the calculated percentage of accurate is 47/50, which is 94% and we are within range of the Ci.

samp %>%
  specify(response = climate_change_affects, success = 'Yes') %>%
  generate(rep=5000, type = 'bootstrap') %>%
  calculate(stat='prop') %>%
  get_ci(level = 0.90)
  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).

Insert your answer here

Ans: high sample size will result in smaller intervals. Smaller sample size results in wider intervals.

  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?

Insert your answer here

Ans: The number of bootstraps affects the accuracy and consistency of the estimation rather than impacting the width of the Ci intervals. Stand error is not being affected by number of bootstraps.