Hypothesis Testing

A Type I error is when: 

• We obtain the wrong test statistic
• We reject the null hypothesis when it is actually true
• We fail to reject the null hypothesis when it's actually false
• We reject the alternate hypothesis when it's actually true

A level of significance of 5% means:

• There's a 5% chance we're wrong
• There's a 5% chance we'll be wrong if we fail to reject the null hypothesis
• There's a 5% chance we'll be wrong if we reject the null hypothesis.
• There's a 5% chance you'll get an A on the test.

Which of the following shows a right-tailed test?

• H₁: µ < 15
• H₀: µ < 15
• H₁: µ > 15
• H₀: µ > 15

A two-tailed test with n = 41 has a test statistic z = 1.56. Which is the correct p-value?

• 0.0594
• Between 0.10 and 0.15
• Between 0.050 and 0.075
• 0.1188

Tags

CCSS.HSS.ID.A.4

A left-tailed test with n = 41 has a test statistic t = 2.72. Which is the correct p-value?

• 0.0033
• Between 0.0005 and 0.005
• Between 0.010 and 0.0010
• 0.9967

Tags

CCSS.HSS.ID.A.4

Identify the correct null hypothesis:

• H₀: p = 0.50
• H₀: µ = 100
• H₀: p = 0.48
• H₀: µ = 96

Tags

CCSS.HSS.IC.A.1

CCSS.HSS.IC.B.4

We conclude "Fail to reject H₀". What could we use as our interpretation?

• The proportion of babies born male is not 0.50.
• There is not enough evidence to say that the proportion of babies born male is different from 0.50.
• There is not enough evidence to say that the proportion of babies born male is 0.50.

Tags

CCSS.HSS.IC.A.1

CCSS.HSS.IC.B.4