# Chapter 13: Samples and surveys

STAT 1010 - Fall 2022

# Learning outcomes

• Know basic sampling vocabulary.
• Understand why randomization is important and describe different sampling methods
• How to sample in R
• Know pitfalls related to sampling

# Intro

• survey : researchers ask questions of a subset of people who belong to a population.
• this subset of the population that reserachers ask is called the sample
• a sample is representative if it seeks to accurately reflect the characteristics of the larger group
• bias occurs in sampling when samples systematically omit a portion of the population
• random samples, where each unit has an equal probability of being choosen, are important

## Predictive policing

• Research question - Can police use crime data from disparate sources to anticipate and prevent future crime?
• population - all crime
• sample - arrests recorded in police database
• Is this a random sample?
• arrests is a surrogate measurement because crime is hard to track

## Predictive policing

• for arrests to occur police must be present
• some areas are over policed, so have more arrests
• this algorithm sends police to those areas
• use census data to predict crime instead of arrests, we may do better

## Census

• sample everyone in the population
• this is often difficult because some individuals are hard to locate, and these people may have certain characterisitics that distinguish them from the rest of the population
• Populations move so getting a perfect measure is hard.
• A census may be more complex than sampling

## Landon vs Roosevelt

• Literary digest - correctly predicted presidential elections from 1916 - 1932 with mock ballots
• 1936 election
• 10 million ballots
• Landon win by landslide with 57%
• 10 million names and addresses in 1936
• poor people were unlikely to have phones, so they over sampled the wealthy
• Gallup predicted a Roosevelt win
• weighting can be used to correct biased samples

# Sampling

## Sampling

• tasting is analogous to exploratory analysis
• stirring helps ensure that the taste is representative because it randomizes
• if we add ingredients and don’t stir we may get a biased sample
• if we generalize and decide that it needs more salt, that’s an inference

## Sampling frame

• lists every member of the population of interest
• can be complex to identify
• sample from registered voters, but really want people who will vote
• hypothetical populations are more complex
• a brewery must sample hops across farmers, and different geographic regions prior to formalizing brewing

## Sampling

• Randomly select cases from the population, where there is no implied connection between the points that are selected.

• Strata are made up of similar observations. We take a simple random sample from each stratum.

• Clusters are usually not made up of homogeneous observations. We take a simple random sample of clusters, and then sample all observations in that cluster. Usually preferred for economical reasons.

## Sampling in R

# SRS
data %>%
slice_sample(n = sample_size)

# Stratified sampling
data %>%
group_by(strata) %>%
slice_sample(n = size_from_each_strata)

# Cluster sampling
random_cluster <- slice_sample(data\$cluster,
n = no_of_clusters)

## Not great sampling methods

• voluntary response