LS 40 (Life Science) Homework 3 Solutions (Lecture 1 W21)updated in Jan 2021

Homework 3 Solutions by Ashley Van Reynolds (adapted from previous solutions) Scoring: 20 Completion Points 10 Accuracy Points: #1, 4a, 5b,c, 6b, c 1. Describe a real-world example of Type I and Type II error (that was not mentioned in lecture or readings). Explain what Type I and Type II error mean in the context of your example. 1 point completion, 1 point accuracy In [3]: %matplotlib inline import matplotlib as mat #import matplotlib import seaborn as sns #import seaborn import numpy as np #import numpy import t as plt Homework 3 Solutions (Lecture 1 W21) 2/5/21, 1:03 AM file:///Users/ana/Downloads/Assignment%20Solutions%2021W/Homew…0Solutions/Homework%203%20Solutions%20(Lecture%201%20W21).html Page 2 of 12 Type I error describes your false positive rate and thus is P(rejecting | True). Type II error describes your false negative rate and thus is P(failing to reject | False). Examples: If a prenatal test comes back positive for Down Syndrome, when your fetus does not have the disorder, that is a false positive (Type I error). If the prenatal test comes back negative, but the fetus actually does have Down Syndrome, that is a false negative (Type II error). A false positive would be if a virus software on your computer incorrectly identifies a harmless program as a malicious one. A false negative would be if the virus software incorectly identifies a malicious program as a harmless one. If a weapon is not detected by airport security, that's a false negative. If something is identified as a weapon that is not a weapon, that's a false positive. If a carbon monoxide detector beeps, even when the level of CO in the room is at a healthy level, then the detector is giving a false positive. If the detector doesn't beep when the level of CO is at a dangerously high level, then the detector is giving a false negative. Examples given in lecture 2.2 that do not receive credit: Man being told he's pregnant (FP) or an obviously pregnant woman being told she's not pregnant (FN) A doctor viewing a kidney scan detects a tumor when there really isn't one (FP) or failing to detect the tumor when there is one (FN)