1.1 Learning objectives

• Understand probability as a measure of uncertainty
• Summarize probabilities with probability distributions
• Describe the difference between population and sample
• Think about uncertainty in sampling
• Summarize uncertainty in sampling: Sampling distribution

2.1 Probability

• How likely a specific event is to happen
  • Value between 0 and 1 (inclusive)
  • Values near 1 = very likely, values near 0 = very unlikely
  • Probability of all possible options adds up to 1
• Flip a coin
  • 50% chance of heads, 50% chance of tails
  • 0.5 probability of heads, 0.5 probability of tails
  • 0.5 + 0.5 = 1

2.2 Probability

• Probability is a way to quantify uncertainty
  • Roll a die
    • How likely is it to roll a 6?
  • Select a playing card from a deck
    • How likely is it to select a heart from the deck?
  • Measure anything
    • How likely is any specific value?

2.3 Roll a die

• 1 in 6 chance of each value: 1, 2, 3, 4, 5, 6
  • Expect to see each value 1/6 of the time
• In very large samples, the probability will be close to 1/6
  • In typical samples, it will be less close

2.4 Roll a die 6 times

set.seed(12345)
die6 <- sample(1:6, size = 6, replace = TRUE)
die6

[1] 6 3 2 4 2 5

table(die6)

die6
2 3 4 5 6 
2 1 1 1 1 

prop.table(table(die6))

die6
        2         3         4         5         6 
0.3333333 0.1666667 0.1666667 0.1666667 0.1666667 

2.5 Roll a die 100 times

set.seed(12345)
die100 <- sample(1:6, size = 100, replace = TRUE)
#die100
table(die100)

die100
 1  2  3  4  5  6 
17 22 19 18 11 13 

prop.table(table(die100))

die100
   1    2    3    4    5    6 
0.17 0.22 0.19 0.18 0.11 0.13 

2.6 Roll a die 10000 times

set.seed(12345)
die10000 <- sample(1:6, size = 10000, replace = TRUE)
#die10000
table(die10000)

die10000
   1    2    3    4    5    6 
1661 1674 1694 1656 1655 1660 

prop.table(table(die10000))

die10000
     1      2      3      4      5      6 
0.1661 0.1674 0.1694 0.1656 0.1655 0.1660 

2.7 Measuring (random) variables

• “Variable”: Anything we measure that can take on more than 1 value
  • In statistics, we would call it a random variable
• Random variable follows some probability distribution
  • There is some probability between 0 and 1 of each possible value
• Table of probabilities works fine for rolling a die
  • What about more complex and/or continuous random variables?

2.8 Probability distributions

• Summarize probabilities across all possible values
• Two broad types of probability distributions
  • Discrete: Probability mass function
  • Continuous: Probability density function
• List of many distributions
  • Some distributions are for outcomes, some are for statistical testing, some are for both

2.9 Probability distribution of die rolls

• Probability of each value is 1/6 = 0.1667

value <- c(1, 2, 3, 4, 5, 6)
probability <- c(1/6, 1/6, 1/6, 1/6, 1/6, 1/6)
rolls <- data.frame(as.factor(value), probability)
ggplot(data = rolls, aes(x = as.factor(value), y = probability)) +
  geom_col() +
  scale_x_discrete(labels = c(1:6)) +
  labs(x = "value")

2.10 Discrete distributions

• Discrete (categorical) values
• Probability for a value = height of the bar for that value
• Sum of height for all bars = 1
• Uniform, Bernoulli, Binomial, Poisson (and negative binomial)

2.11 Uniform distribution (discrete)

• All possible values are equally likely (uniform probability)
  • Die roll, coin flip
• Notation: \(X \sim U\{a,b\}\)
• \(E(X) = \frac{a+b}{2}\)
• \(Var(X) = \frac{(b - a + 1)^2 - 1}{12}\)

2.12 Uniform: 6 sided die

unid_dat <- data.frame(x = 1:6, y = 1/6)

ggplot(data = unid_dat, aes(x = x, y = y)) +
  geom_col() +
  scale_x_continuous(breaks = 1:6) +
  ylim(0,1) +
  labs(x = "X", y = "P(X = x)")

2.13 Bernoulli distribution

• Single trials, each with two possible values (0 = failure, 1 = success)
  • Alive or dead, pass or fail
• Notation: \(X \sim B(p)\), where \(p\) is the probability of success
• Distribution: \(P(X = x) = p^x (1 - p)^{1 -x}\)
• \(E(X) = p\)
• \(Var(X) = p(1-p)\)

2.14 Bernoulli: \(p\) = 0.3

bern_dat <- data.frame(x = 0:1, y = dbinom(0:1, 1, 0.3))
ggplot(data = bern_dat, aes(x = x, y = y)) +
  geom_col() +
  scale_x_continuous(breaks = 0:1) +
  ylim(0,1) +
  labs(x = "X", y = "P(X = x)")

2.15 Binomial distribution

• The sum of \(m\) Bernoulli trials (each of which is 0/1)
  • Deaths in a group, passes in a class, voting (yes/no)
    
• Notation: \(X \sim Bin(m, p)\)
• Distribution: \(P(X = x) = {m \choose x} p^x (1 - p)^{m -x}\)
  • \({m \choose x}\) is “\(m\) choose \(x\)”
• \(E(X) = mp\)
• \(Var(X) = mp(1-p)\)

2.16 Binomial: \(m\) = 15, \(p\) = 0.5

binom_dat <- data.frame(x = 0:15, y = dbinom(0:15, 15, 0.5))
ggplot(data = binom_dat, aes(x = x, y = y)) +
  geom_col() +
  scale_x_continuous(breaks = 0:15) +
  ylim(0,1) +
  labs(x = "X", y = "P(X = x)")

2.17 Binomial: \(m\) = 25, \(p\) = 0.1

binom_dat <- data.frame(x = 0:15, y = dbinom(0:15, 25, 0.1))
ggplot(data = binom_dat, aes(x = x, y = y)) +
  geom_col() +
  scale_x_continuous(breaks = 0:15) +
  ylim(0,1) +
  labs(x = "X", y = "P(X = x)")

2.18 Poisson distribution

• Distribution of counts in a fixed period of time
  • Gene expression, earthquakes, days per month in pain
• Notation: \(X \sim Pois(\lambda)\), where \(\lambda\) is the mean and variance
• Distribution: \(P(X = x) = \frac{\lambda^x e^{-\lambda}}{x!}\)
• \(E(X) = \lambda\)
• \(Var(X) = \lambda\)
• Negative binomial distribution relaxes the mean-variance equality

2.19 Poisson: \(\lambda\) = 2.5

pois_dat <- data.frame(x = 0:15, y = dpois(0:15, 2.5))
ggplot(data = pois_dat, aes(x = x, y = y)) +
  geom_col() +
  scale_x_continuous(breaks = 0:15) +
  ylim(0,1) +
  labs(x = "X", y = "P(X = x)")

2.20 Poisson: \(\lambda\) = 5

pois_dat <- data.frame(x = 0:15, y = dpois(0:15, 5))
ggplot(data = pois_dat, aes(x = x, y = y)) +
  geom_col() +
  scale_x_continuous(breaks = 0:15) +
  ylim(0,1) +
  labs(x = "X", y = "P(X = x)")

2.21 Continuous distributions

• Continuous (numeric) values
• Height of the line for a value is not probability for that value
  • Area under the curve between two values = probability between those values
• Area under the entire curve = 1
• Uniform, Normal (Gaussian), \(t\), \(F\), Chi-square (\(\chi^2\))

2.22 Uniform distribution (continuous)

• All possible values are equally likely (uniform probability)
  • Wait times
• Notation: \(X \sim U(a,b)\)
• Distribution: \(f(x) = \frac{1}{b-a}\) for \(a \le x \le b\)
  • \(f(x) = 0\) for \(x < a\) or \(x > b\)
• \(E(X) = \frac{a+b}{2}\)
• \(Var(X) = \frac{(b - a)^2}{12}\)

2.23 Uniform: between 0 and 5

x <- seq(-1, 13, by = 0.01)
y <- dunif(x, 0, 5)
uni_dat <- data.frame(x, y)
ggplot(data = uni_dat, aes(x = x, y = y)) +
  geom_line() +
  scale_x_continuous(limits = c(-1, 13), breaks = 0:12) +
  ylim(0,.25) + 
  labs(x = "X", y = "f(x)")

2.24 Uniform: between 3 and 12

uni_dat <- data.frame(x = seq(-1, 13, by = 0.01), y = dunif(x, 3, 12))
ggplot(data = uni_dat, aes(x = x, y = y)) +
  geom_line() +
  scale_x_continuous(limits = c(-1, 13), breaks = 0:12) +
  ylim(0,.25) + 
  labs(x = "X", y = "f(x)")

2.25 Normal distribution

• Gaussian, bell-shaped curve
  • Height, random error, birth weight, SAT scores, statistical tests
• Notation: \(X \sim \mathcal{N}(\mu, \sigma^2)\)
• Distribution: \(f(x) = \frac{1}{\sigma\sqrt{2\pi}} e^{-\frac{1}{2}\left(\frac{x - \mu}{\sigma}\right)}\)
• \(E(X) = \mu\)
• \(Var(X) = \sigma^2\)
• Modifications: skew-normal, log-normal

2.26 Normal: \(\mu\) = 0, \(\sigma^2\) = 1

ggplot(data.frame(x = c(-3, 10)), aes(x)) +
  stat_function(fun = dnorm, args = list(mean = 0, sd = 1), geom = "line") +
  ylim(0,.5) + 
  scale_x_continuous(breaks = -3:10) +
  labs(x = "X", y = "f(x)") 

2.27 Normal: \(\mu\) = 5, \(\sigma^2\) = 2

ggplot(data.frame(x = c(-3, 10)), aes(x)) +
  stat_function(fun = dnorm, args = list(mean = 5, sd = sqrt(2)), geom = "line") +
  ylim(0,.5) + 
  scale_x_continuous(breaks = -3:10) +
  labs(x = "X", y = "f(x)") 

2.28 (Student’s) \(t\) distribution

• Sampling distribution for many statistical tests
  • Approximation to normal distribution in small samples
  • Closer as degrees of freedom increase
• Notation: \(X \sim t(df, ncp)\) or \(X \sim t(\mu, \sigma^2, \nu)\)
• Distribution: See here if you’re interested
• \(E(X) = 0\) (when \(ncp\) = 0)
• \(Var(X) = \frac{df}{df-2}\) (when \(ncp\) = 0)

2.29 \(t\): \(df\) = 10

ggplot(data.frame(x = c(-3, 3)), aes(x)) +
  stat_function(fun = dt, args = list(df = 10, ncp = 0), geom = "line") +
  ylim(0,.5) + 
  scale_x_continuous(breaks = -3:3) +
  labs(x = "X", y = "f(x)") 

2.30 \(t\): \(df\) = 150

ggplot(data.frame(x = c(-3, 3)), aes(x)) +
  stat_function(fun = dt, args = list(df = 150, ncp = 0), geom = "line") +
  ylim(0,.5) + 
  scale_x_continuous(breaks = -3:3) +
  labs(x = "X", y = "f(x)") 

2.31 \(\mathcal{N}(0, 1)\) (red) vs \(t(10)\) (blue)

ggplot(data.frame(x = c(-3, 3)), aes(x)) +
  stat_function(fun = dt, args = list(df = 10, ncp = 0), geom = "line", color = "blue", linewidth = 0.75) +
  stat_function(fun = dnorm, args = list(mean = 0, sd = 1), geom = "line", color = "red", linewidth = 0.75) +
  ylim(0,.5) + 
  scale_x_continuous(breaks = -3:3) +
  labs(x = "X", y = "f(x)") 

2.32 \(F\) distribution

• Sampling distribution for many statistical tests
• Notation: \(X \sim F(d_1, d_2)\)
• Distribution: See here if you’re interested
• \(E(X) = \frac{d_2}{d_2-2}\)
• \(Var(X) = \frac {2\,d_{2}^{2}\,(d_{1}+d_{2}-2)}{d_{1}(d_{2}-2)^{2}(d_{2}-4)}\)

2.33 \(F\): \(d_1\) = 1, \(d_2 = 49\)

ggplot(data.frame(x = c(0, 10)), aes(x)) +
  stat_function(fun = df, args = list(df1 = 1, df2 = 49), geom = "line") +
  ylim(0,1) + 
  scale_x_continuous(breaks = 0:10) +
  labs(x = "X", y = "f(x)") 

2.34 \(F\): \(d_1\) = 5, \(d_2 = 99\)

ggplot(data.frame(x = c(0, 10)), aes(x)) +
  stat_function(fun = df, args = list(df1 = 5, df2 = 99), geom = "line") +
  ylim(0,1) + 
  scale_x_continuous(breaks = 0:10) +
  labs(x = "X", y = "f(x)") 

2.35 Chi-square (\(\chi^2\)) distribution

• Sampling distribution for many statistical tests
• Notation: \(X \sim \chi^2(k)\)
• Distribution: See here if you’re interested
• \(E(X) = k\)
• \(Var(X) = 2k\)

2.36 \(\chi^2\): \(k\) = 1

ggplot(data.frame(x = c(0, 15)), aes(x)) +
  stat_function(fun = dchisq, args = list(df = 1), geom = "line") +
  ylim(0,1) + 
  scale_x_continuous(breaks = 0:15) +
  labs(x = "X", y = "f(x)") 

2.37 \(\chi^2\): \(k\) = 5

ggplot(data.frame(x = c(0, 15)), aes(x)) +
  stat_function(fun = dchisq, args = list(df = 5), geom = "line") +
  ylim(0,1) + 
  scale_x_continuous(breaks = 0:15) +
  labs(x = "X", y = "f(x)") 

2.38 Summary: Probability distributions

• Probability is a way to quantify uncertainty
• Probability distributions summarize probabilities across all possible values of a random variable
• Variety of discrete and continuous probability distributions
  • Some for actual variables, some for statistical tests

3.1 How does a study work?

flowchart LR
  A(Population) --> B{Sampling}
  B{Sampling} --> D[Sample]

flowchart RL
  C{Inference} --> A(Population)
  D[Sample] --> C{Inference}

3.2 How does a study work?

Step 1. Identify a population that you’re interested in

• This can be very broad
  • All adults in the United States
• Or very specific
  • All adults living in Southern California who are > 65 years old who have had a heart attack in 2015

3.3 How does a study work?

Step 2. Sample from the population

• Select a subset of individuals from the population
  • Do your study on the sample
    • Randomize to groups
    • Estimate means
    • Statistical tests

3.4 Who cares about the sample?

• We don’t actually care about the sample
• The sole purpose of the sample is to let us make inferences about the population
• Any individual sample doesn’t matter
  • Any individual in the sample doesn’t matter

4.1 Uncertainty in sampling

• Major problem with samples: They’re not the population that you’re actually interested in
  • They are not fully representative of the population
  • Do not reflect the full variability of the population
  • May be biased (intentionally or not)
• Different samples are also different from one another

4.2 Uncertainty in sampling

• Create a (normally distributed) population of 1 million values with a mean of 0 and standard deviation of 1
  • \(pop \sim \mathcal{N}(0,1)\)

pop <- rnorm(1000000, 0, 1)
mean(pop)

[1] 0.0000667581

sd(pop)

[1] 1.001029

4.3 Sample 1

• Draw a sample from the population of size N = 100

sample1 <- sample(pop, 100)
mean(sample1)

[1] 0.06610631

sd(sample1)

[1] 0.968169

4.4 Sample 2

• Draw another sample from the population of size N = 100

sample2 <- sample(pop, 100)
mean(sample2)

[1] -0.1525419

sd(sample2)

[1] 0.9083531

4.5 Sample 3

• Draw another sample from the population of size N = 100

sample3 <- sample(pop, 100)
mean(sample3)

[1] 0.08553226

sd(sample3)

[1] 0.9714634

4.6 Sampling variability

• Each sample mean is different from the other sample means
• Each sample SD is different from the other sample SDs
• Each sample mean is different from the population mean
• Each sample SD is different from the population SD

5.1 Sampling distribution

• Sampling distribution is a probability distribution
  • Distribution of a statistic (often the mean)
  • Under repeated sampling (or all possible samples) from the population
  • With the same sample size (N)

5.2 How do we get sampling distributions?

• Mathematically derived
  • Common distributions, like Normal, Bernoulli, etc.
• Computationally derived
  • Monte Carlo simulations, bootstrapping
  • Literally, repeatedly sample from the population, save the test statistic from each sample, make a distribution (i.e., histogram) of those values

5.3 Sampling distribution

• Normally distributed population: \(\mathcal{N}(0,1)\)
• Sampling distribution of the mean
• Different sample sizes
• This situation can use either method
  • Mathematically derived: \(\bar{X} \sim \mathcal{N}(\mu,\frac{\sigma^2}{n})\)
  • Simulation based

5.4 Sampling distribution: \(n\) = 10, 50, 100

ggplot(data.frame(x = c(-2, 2)), aes(x)) +
  #stat_function(fun = dnorm, args = list(mean = 0, sd = 1), geom = "line") +
  stat_function(fun = dnorm, args = list(mean = 0, sd = 1/sqrt(10)), geom = "line", color = "red") +
  stat_function(fun = dnorm, args = list(mean = 0, sd = 1/sqrt(50)), geom = "line", color = "green") +
  stat_function(fun = dnorm, args = list(mean = 0, sd = 1/sqrt(100)), geom = "line", color = "purple") +
  #ylim(0,1) + 
  scale_x_continuous(breaks = -2:2) +
  labs(x = "X", y = "f(x)") 

5.5 Sampling distribution via simulation: \(n\) = 10

library(infer)
means10 <- data.frame(pop) %>%
  rep_sample_n(size = 10, reps = 10000, replace = TRUE) %>%
  summarise(x_bar = mean(pop))

ggplot(data = means10, aes(x = x_bar)) +
  geom_histogram(bins = 30) +
  xlim(-1.5,1.5)

5.6 Sampling distribution via simulation: \(n\) = 50

library(infer)
means10 <- data.frame(pop) %>%
  rep_sample_n(size = 50, reps = 10000, replace = TRUE) %>%
  summarise(x_bar = mean(pop))

ggplot(data = means10, aes(x = x_bar)) +
  geom_histogram(bins = 30) +
  xlim(-1.5, 1.5) 

5.7 Sampling distribution via simulation: \(n\) = 100

library(infer)
means10 <- data.frame(pop) %>%
  rep_sample_n(size = 100, reps = 10000, replace = TRUE) %>%
  summarise(x_bar = mean(pop))

ggplot(data = means10, aes(x = x_bar)) +
  geom_histogram(bins = 30) +
  xlim(-1.5, 1.5)

5.8 Why do we care about this?

• Sampling distribution of the mean gives the standard error of the mean
  • Standard deviation of the sampling distribution
  • Tells us about sampling variability in the test statistic
  • Across all samples from this population, how variable can we expect the test statistic to be?
  • Context
  • Used in significance testing

6.1 In-class activities

• Explore some sampling distributions using simulation
• See how changing the population distribution and sample size affect the sampling distribution

6.2 Next week

• Estimation
  • Point estimates vs interval estimates
  • Confidence intervals
  • Inference by eye?