Page 56 - Quantitative Data Analysis

Quantitative Data Analysis
Simply Explained Using SPSS

Type I and Type II Error Example - 1

Suppose a researcher is interested in examining the safety of a drug.
– H 0 : The drug is unsafe.
– H 1 : The drug is safe.

• What is the type I error?
– Reject H 0 when true; more specifically, conclude drug is safe when in-fact
it is unsafe.

• What is the type II error?
– Fail to reject H0 when it is actually false; more specifically, conclude drug
is unsafe when it is actually safe.

• In this example, which error is more harmful?
– Would like want to make the likelihood of a Type I Error to be very small.

Type I and Type II Error Example - 2

• Suppose a medical researcher is testing blood to determine if it is
appropriate for use (contaminated or not contaminated)
– H 0 : The blood is not contaminated.
– H 1 : The blood is contaminated.

• What is the type I error?
– Conclude blood is contaminated when in fact it is not.

• What is the type II error?

– Conclude blood is not contaminated when it is contaminated.

• In this example, which error is more harmful?
– Type II Error is probably worse here.

• What if there was a blood shortage?
– If there was a serious shortage, you may take the increased type II error
risk as opposed to not getting the blood transfusion.

The Theory and Applications of Statistical Inferences 40