19.1 Types of hypothesis testing

\(H_0 : \theta = \theta_0\)

\(H_1 : \theta \neq \theta_0\)

How far away / extreme \(\theta\) can be if our null hypothesis is true

Assume that our likelihood function for q is \(L(q) = q^{30}(1-q)^{70}\) Likelihood function

q = seq(0,1,length=100)
L= function(q){q^30 * (1-q)^70}
plot(q,L(q),ylab="L(q)",xlab="q",type="l")

Log-Likelihood function

q = seq(0,1,length=100)
l= function(q){30*log(q) + 70 * log(1-q)}
plot(q,l(q)-l(0.3),ylab="l(q) - l(qhat)",xlab="q",type="l")
abline(v=0.2)

(Fox 1991)

typically, The likelihood ratio test (and Lagrange Multiplier (Score)) performs better with small to moderate sample sizes, but the Wald test only requires one maximization (under the full model).