% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data-rosling_responses.R
\docType{data}
\name{rosling_responses}
\alias{rosling_responses}
\title{Sample Responses to Two Public Health Questions}
\format{
A data frame with 278 rows and 3 variables:
\describe{
\item{question}{ID for the question being posed.}
\item{response}{Noting whether the response was \code{correct}
or \code{incorrect}.}
\item{prob_random_correct}{The probability the person would have guessed
the answer correctly if they were guessing completely randomly.}
}
}
\source{
The samples we describe are plausible based on the exact rates observed in larger samples.
For more info on the actual rates observed, visit \url{https://www.gapminder.org}.

Another relevant reference is a book by
Hans Rosling, Anna Rosling Ronnlund, and Ola Rosling called
\href{https://www.gapminder.org/factfulness-book/}{Factfulness}.
}
\usage{
rosling_responses
}
\description{
Public health has improved and evolved, but has the public's knowledge
changed with it? This dataset explores sample responses for two survey
questions posed by Hans Rosling during lectures to a wide array of
well-educated audiences.
}
\examples{

frac_correct <- tapply(
  rosling_responses$response == "correct",
  rosling_responses$question,
  mean
)
frac_correct
n <- table(rosling_responses$question)
n
expected <- tapply(
  rosling_responses$prob_random_correct,
  rosling_responses$question,
  mean
)

# Construct confidence intervals.
se <- sqrt(frac_correct * (1 - frac_correct) / n)
# Lower bounds.
frac_correct - 1.96 * se
# Upper bounds.
frac_correct + 1.96 * se

# Construct Z-scores and p-values.
z <- (frac_correct - expected) / se
pt(z, df = n - 1)
}
\keyword{datasets}
