Making decisions

Lecture 25

Dr. Mine Çetinkaya-Rundel

Duke University
STA 199 - Fall 2025

December 2, 2025

Warm-up

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Announcements

HW:
- HW 5 accepted until Wed, Dec 3 at 11:59 pm without penalty
- HW 6 due at 11:59 pm on Fri, Dec 6, accepted until Sun, Dec 7 at 11:59 pm without penalty
Final exam:
- Classroom: Half of you will take it in this room (Bio Sci 111) the other half in (Physics 128), check your email for your classroom assignment
- Review: During reading period, date/time TBA
- Office hours: TBA soon as review session is scheduled

From last time

Participate 📱💻

Which of the following is true about confidence intervals?

They’re for a sample statistic.
They’re for a population parameter.
They can be either for both a sample statistic or a population parameter.
They’re neither for a sample statistic nor a population parameter.

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Why do we construct confidence intervals?

To estimate plausible values of a parameter of interest, e.g.,

a slope (\(\beta_1\))
a mean (\(\mu\))
a proportion (\(p\))
etc.

What is bootstrapping?

Bootstrapping is a statistical procedure that resamples(with replacement) a single data set to create many simulated samples.
We then use these simulated samples to quantify the uncertainty around the sample statistic we’re interested in, e.g., a slope (\(b_1\)), a mean (\(\bar{x}\)), a proportion (\(\hat{p}\)).

Setup

library(tidyverse)
library(tidymodels)
library(openintro)

Computing the CI for the slope I

Calculate the observed slope:

observed_fit <- duke_forest |>
  specify(price ~ area) |>
  fit()

observed_fit

# A tibble: 2 × 2
  term      estimate
  <chr>        <dbl>
1 intercept  116652.
2 area          159.

Computing the CI for the slope II

Take 1000 bootstrap samples and fit models to each one:

set.seed(1120)

boot_fits <- duke_forest |>
  specify(price ~ area) |>
  generate(reps = 1000, type = "bootstrap") |>
  fit()

boot_fits

# A tibble: 2,000 × 3
# Groups:   replicate [1,000]
   replicate term      estimate
       <int> <chr>        <dbl>
 1         1 intercept   47819.
 2         1 area          191.
 3         2 intercept  144645.
 4         2 area          134.
 5         3 intercept  114008.
 6         3 area          161.
 7         4 intercept  100639.
 8         4 area          166.
 9         5 intercept  215264.
10         5 area          125.
# ℹ 1,990 more rows

What does each observation on the plot represent?

Resample, with replacement, from the original data
Do this reps = 1000 times
Calculate the summary statistic of interest in each of these samples

Computing the CI for the slope III

Percentile method: Compute the 95% CI as the middle 95% of the bootstrap distribution:

get_confidence_interval(
  boot_fits,
  point_estimate = observed_fit,
  level = 0.95,
  type = "percentile" # default method
)

# A tibble: 2 × 3
  term      lower_ci upper_ci
  <chr>        <dbl>    <dbl>
1 area          87.5     216.
2 intercept -24293.   300646.

Participate 📱💻

If you want to be very certain (i.e., more confident) that you capture the population parameter, should we use a wider or a narrower interval?

Wider
Narrower
Depends on the situation

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Precision vs. accuracy

What drawbacks are associated with using a wider interval?

Precision vs. accuracy

How can we get best of both worlds – high precision and high accuracy?

Recap

Population: Complete set of observations of whatever we are studying, e.g., people, tweets, photographs, etc. – population size = \(N\)
Sample: Subset of the population, ideally random and representative – sample size = \(n\)
Sample statistic \(\ne\) population parameter, but if the sample is good, it can be a good estimate
Statistical inference: Discipline that concerns itself with the development of procedures, methods, and theorems that allow us to extract meaning and information from data that has been generated by stochastic (random) process
We report the estimate with a confidence interval, and the width of this interval depends on the variability of sample statistics from different samples from the population
Since we can’t continue sampling from the population, we bootstrap from the one sample we have to estimate sampling variability

An alternative approach

Standard error method: Compute the 95% CI as the observed slope plus/minus ~2 * standard error (the standard deviation of the bootstrap distribution):

get_confidence_interval(
  boot_fits,
  point_estimate = observed_fit,
  level = 0.95,
  type = "se"
)

# A tibble: 2 × 3
  term      lower_ci upper_ci
  <chr>        <dbl>    <dbl>
1 area          95.5     223.
2 intercept -42990.   276295.

Margin of error

That quantity (~2 * standard error) is called the margin of error, e.g.,

On the horizon…

In this class you learned how to construct a confidence interval (i.e., calculate the margin of error) using a computational method called bootstrapping.
The bootstrap distributions you constructed (given enough reps – repeated samples) were unimodal and symmetric around the observed statistic.
This is not a happenstance! And there is theory behind it… It’s called the Central Limit Theorem!
You can learn about the Central Limit Theorem and theory-based methods for constructing confidence intervals (and other inference procedures) in future stats courses.

Syntax notes

Bootstrapping for categorical data
- specify(response = x, success = "success level")
- calculate(stat = "prop")

Bootstrapping for other stats
- calculate() documentation: infer.tidymodels.org/reference/calculate.html
- infer pipelines: infer.tidymodels.org/articles/observed_stat_examples.html

Hypothesis testing

A hypothesis test is a statistical technique used to evaluate competing claims using data

Null hypothesis, \(H_0\): An assumption about the population. “There is nothing going on.”
Alternative hypothesis, \(H_A\): A research question about the population. “There is something going on”.

Note

Hypotheses are always at the population level!

Setting hypotheses

Null hypothesis, \(H_0\): “There is nothing going on.” The slope of the model for predicting the prices of houses in Duke Forest from their areas is 0, \(\beta_1 = 0\).
Alternative hypothesis, \(H_A\): “There is something going on”. The slope of the model for predicting the prices of houses in Duke Forest from their areas is different than, \(\beta_1 \ne 0\).

Hypothesis testing “mindset”

Assume you live in a world where null hypothesis is true: \(\beta_1 = 0\).
Ask yourself how likely you are to observe the sample statistic, or something even more extreme, in this world:

\[P \big( b_1 \leq -159~or~b_1 \geq 159 ~|~ \beta_1 = 0 \big)\]

Hypothesis testing as a court trial

Null hypothesis, \(H_0\): Defendant is innocent
Alternative hypothesis, \(H_A\): Defendant is guilty

Present the evidence: Collect data

Judge the evidence: “Could these data plausibly have happened by chance if the null hypothesis were true?”
- Yes: Fail to reject \(H_0\)
- No: Reject \(H_0\)

Hypothesis testing framework

Start with a null hypothesis, \(H_0\), that represents the status quo
Set an alternative hypothesis, \(H_A\), that represents the research question, i.e. what we’re testing for
Conduct a hypothesis test under the assumption that the null hypothesis is true and calculate a p-value (probability of observed or more extreme outcome given that the null hypothesis is true)
- if the test results suggest that the data do not provide convincing evidence for the alternative hypothesis, stick with the null hypothesis
- if they do, then reject the null hypothesis in favor of the alternative

Calculate observed slope

… which we have already done:

observed_fit <- duke_forest |>
  specify(price ~ area) |>
  fit()

observed_fit

# A tibble: 2 × 2
  term      estimate
  <chr>        <dbl>
1 intercept  116652.
2 area          159.

Simulate null distribution

set.seed(20251202)
null_dist <- duke_forest |>
  specify(price ~ area) |>
  hypothesize(null = "independence") |>
  generate(reps = 1000, type = "permute") |>
  fit()

View null distribution

null_dist

# A tibble: 2,000 × 3
# Groups:   replicate [1,000]
   replicate term       estimate
       <int> <chr>         <dbl>
 1         1 intercept 594889.  
 2         1 area         -12.6 
 3         2 intercept 477930.  
 4         2 area          29.5 
 5         3 intercept 581950.  
 6         3 area          -7.93
 7         4 intercept 487542.  
 8         4 area          26.0 
 9         5 intercept 643406.  
10         5 area         -30.0 
# ℹ 1,990 more rows

Visualize null distribution

visualize(null_dist)

Visualize null distribution + p-value

visualize(null_dist) +
  shade_p_value(
    obs_stat = observed_fit, 
    direction = "two-sided"
  )

Get p-value

null_dist |>
  get_p_value(obs_stat = observed_fit, direction = "two-sided")

Warning: Please be cautious in reporting a p-value of 0. This result is an
approximation based on the number of `reps` chosen in the
`generate()` step.
ℹ See `get_p_value()` (`?infer::get_p_value()`) for more information.
Please be cautious in reporting a p-value of 0. This result is an
approximation based on the number of `reps` chosen in the
`generate()` step.
ℹ See `get_p_value()` (`?infer::get_p_value()`) for more information.

# A tibble: 2 × 2
  term      p_value
  <chr>       <dbl>
1 area            0
2 intercept       0

Make a decision

Based on the p-value calculated, what is the conclusion of the hypothesis test?