This semester, I used the new ModernDive textbook to teach introductory statistics for executive MPA students at BYU, and it’s been absolutely delightful. The book’s approach to teaching statistics follows a growing trend (led by Mine Çetinkaya-Rundel, Alison Hill, and others) of emphasizing data and simulations instead of classical probability theory and complex statistical tests.
Where this approach really shines is with hypothesis testing. This is the core of inferential statistics, but it’s really hard for students to wrap their head around how to reject null hypotheses and interpret p-values. Even seasoned scientists struggle with explaining what p-values mean. This stuff is hard.
ModernDive borrows from Allen Downey’s philosophy that there is only one statistical test and that at their core, all statistical tests (be they t-tests, chi-squared tests, signed Wilcoxon rank tests, etc.) follow the same universal pattern:
- Step 1: Calculate a sample statistic, or \(\delta\). This is the main measure you care about: the difference in means, the average, the median, the proportion, the difference in proportions, the chi-squared value, etc.
- Step 2: Use simulation to invent a world where \(\delta\) is null. Simulate what the world would look like if there was no difference between two groups, or if there was no difference in proportions, or where the average value is a specific number.
- Step 3: Look at \(\delta\) in the null world. Put the sample statistic in the null world and see if it fits well.
- Step 4: Calculate the probability that \(\delta\) could exist in null world. This is the p-value, or the probability that you’d see a \(\delta\) at least that high in a world where there’s no difference.
- Step 5: Decide if \(\delta\) is statistically significant. Choose some evidentiary standard or threshold for deciding if there’s sufficient proof for rejecting the null world. Standard thresholds (from least to most rigorous) are 0.1, 0.05, and 0.01.
That’s all. Five steps. No need to follow complicated flowcharts to select the best and most appropriate statistical test. No need to run a bunch of tests to see if you need to pool variances or leave them separate. Calculate a number, simulate a null world, and decide if that number is significantly different from what is typically seen in the null world. Voila!
infer package in R makes this process explicit, easy, and intuitive.
In December 2018 (just two days ago!), the World Bank announced a new Worldwide Bureaucracy Indicators Database, with dozens of measures of public sector effectiveness. The data includes variables that measure the proportion of women employed in both the private and the public sectors. Is there a difference between these two sectors? Do more women work in the public sector than the private sector? Instead of consulting a flow chart to find the right test that meets our assumptions and data limitations, we just simulate our way to a p-value and test to see if the difference in median proportions in each sector is significantly different from zero.
We first download a CSV of the WWBI data from the World Bank. In its raw form, it’s kind of a mess, with a row for each indicator in each country (so Afghanistan has a row for women private sector employment, one for women public sector employment, and so on), and columns for each year. This heavily commented code will load and clean and rearrange the WWBI data into something we can analyze and plot.
# Load libraries library(tidyverse) library(ggridges) library(scales) library(infer) set.seed(1234) # Make all random draws reproducible # https://datacatalog.worldbank.org/dataset/worldwide-bureaucracy-indicators wwbi <- read_csv("WWBIData.csv") # Create a list of indicators we want to work with indicators <- c( "BI.PWK.PRVS.FE.ZS", # females as share of private paid employees "BI.PWK.PUBS.FE.ZS" # females as share of public paid employees ) # Make a small, cleaner subset of the WWBI data wwbi_small <- wwbi %>% # Only select the columns we care about select(country = `Country Name`, country_code = `Country Code`, indicator = `Indicator Code`, starts_with("20")) %>% # Keep only the indicators we care about filter(indicator %in% indicators) %>% # Gather all the year-based columns into two long columns gather(year, value, starts_with("20")) %>% # Spread the data back out so that there are columns for each indicator spread(indicator, value) %>% # Make these indicator names human readable rename(share_female_private = `BI.PWK.PRVS.FE.ZS`, share_female_public = `BI.PWK.PUBS.FE.ZS`) %>% # Amid all the gathering and spreading, every column has become a character. # This converts the year and all the share_* variables back to numbers mutate_at(vars(year, starts_with("share")), as.numeric) wwbi_2012 <- wwbi_small %>% filter(year == 2012) %>% # Get rid of rows that are missing data in the share_* columns drop_na(starts_with("share")) %>% # Make this tidy and long, with a column for private or public gather(sector, proportion, starts_with("share")) %>% # Make these values even nicer mutate(sector = recode(sector, share_female_private = "Women in private sector", share_female_public = "Women in public sector"))
First, we’ll use a ridge plot (or overlapping density plots) to see if there’s a difference in the distribution of employment across these two sectors. We include
quantile_lines = TRUE and
quantiles = 2 to draw the median of each distribution.
ggplot(wwbi_2012, aes(x = proportion, y = sector, fill = sector)) + stat_density_ridges(quantile_lines = TRUE, quantiles = 2, scale = 3, color = "white") + scale_fill_manual(values = c("#98823c", "#9a5ea1"), guide = FALSE) + scale_x_continuous(labels = percent_format(accuracy = 1)) + labs(x = "Percent of women employed in the sector", y = NULL) + theme_minimal() + theme(panel.grid.minor = element_blank())
It looks like there’s a difference here—the median percent of women in the public sector looks like it’s between 40–50%, while the median percent of women in the private sector is between 30–40%.
We can use the
infer package to determine the exact difference in the medians of these distributions. We use a formula in
specify() to indicate which variables we want to be our response (or y) and which we want to be our explanatory (or x) variables, and then we
calculate() the difference in median values. The
order argument tells
infer to subtract the public values from the private values.
diff_prop <- wwbi_2012 %>% specify(proportion ~ sector) %>% calculate("diff in medians", order = c("Women in private sector", "Women in public sector")) diff_prop ## # A tibble: 1 x 1 ## stat ## <dbl> ## 1 -0.102
The difference in the median proportions here is 10.2%. More women appear to be employed in the public sector. But because of sampling issues, is there a possibility that this 10% difference could potentially be zero? Could it be positive instead of negative?
To test this, we simulate a world where the actual difference in medians between these two sectors is zero. We then plot that null distribution, place the observed 10.2% difference in it, and see how well it fits. Here we follow the same
specify() %>% calculate() pattern, but introduce two new steps. We first
hypothesize() that the two sectors are independent of each other and that there’s no difference between the two. We then
generate() a null distribution based on permutation (essentially shuffling the private/public labels within the existing data 5,000 times), and then calculate the difference in medians for the two groups.
pub_private_null <- wwbi_2012 %>% specify(proportion ~ sector) %>% hypothesize(null = "independence") %>% generate(reps = 5000) %>% calculate("diff in medians", order = c("Women in private sector", "Women in public sector")) # Conveniently, the visualize() function that comes with infer returns a ggplot # object, so we can continue to add ggplot layers to enhance and clean the plot pub_private_null %>% visualize(obs_stat = diff_prop) + labs(x = "Difference in median proportion\n(Women in private sector − women in public sector)", y = "Count", subtitle = "Red line shows observed difference in median proportions") + scale_x_continuous(labels = percent_format(accuracy = 1)) + theme_minimal() + theme(panel.grid.minor = element_blank())
This red line is pretty far in the left tail of the distribution and seems atypical. We can calculate the probability of seeing a difference as big as 10.2% with the
get_pvalue() function. Because we care about differences in means or medians (and because the difference could be positive if we flipped the order of subtracting the two groups—public−private instead of private−public), we specify
direction = "both" to get a two-tailed p-value.
pub_private_null %>% get_pvalue(obs_stat = diff_prop, direction = "both") ## # A tibble: 1 x 1 ## p_value ## <dbl> ## 1 0.00440
The p-value is 0.0044, which means that there’s a 0.44% chance of seeing a difference at least as large as 10.2% in a world where there’s no difference. That’s pretty strong evidence, and I’d feel confident declaring that there’s a statistically significant difference between sectoral employment patterns for women, with more women working in public sector jobs than in the private sector.
And that’s it! No flow charts. No complex assumption checking. Using the
infer package in R, we brute forced-ly simulated our way to a p-value that is actually interpretable, and now we know that women tend to work in the public sector more than in the private sector. Magic!