Transcript Decision Errors - University of Toronto

Decision Errors and Power
• When we perform a statistical test we hope that our decision will
be correct, but sometimes it will be wrong. There are two types
of incorrect decisions. To help distinguish these two types of
error, we give them specific names.
• The error made by rejecting the null hypothesis H0
(accepting Ha) when in fact H0 is true is called a type I error.
• The probability of making a type I error is denoted by .
• The error made by accepting the null hypothesis H0
(rejecting Ha) when in fact H0 is false is called a type II error.
• The probability of making a type II error is denoted by .
• The probability that a fixed level  significant test will reject H0
when a particular alternative value of the parameter is true is
called the power of the test to detect that alternative.
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• Significance and type I error
The significance level  of any fixed level test is the
probability of a Type I error. That is  is the probability that the
test will reject the null hypothesis H0 when H0 is in fact true.
• Power and Type II error
The power of a fixed level test against a particular alternative is
Power = 1- β = 1- P( accepting H0 when H0 is false) =
= P( rejecting H0 when H0 is false)
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Ways to increase Power
• Increase α. When we increase α the strength of evidence
required for the rejection is less.
• Consider a particular Ha that is farther away from μ0.
Values of μ that are in Ha but lie close to μ0 are harder to
detect (lower power) then values of μ that are far from μ0.
• Increase sample size. More data will provide more information
about the population so we have a better chance of
distinguishing values of µ.
• Decrease σ. This has the same effect as increasing the sample
size: more information about µ. Improving the measurement
process and restricting attention to a subpopulation are two
common ways to decrease σ.
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Example
• We are interested to learn about the mean contents of cola
bottles and want to test the following hypotheses:
H0:  = 300
Ha:  < 300.
• The sample size is n = 6, and the population is assumed to
have a normal distribution with  = 3. A 5% significance test
rejects H0 if z ≤ Z0.05 = -1.645 where the test statistic z is
z  x 300
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• Power calculations help us see how large a shortfall in the
bottle contents the test can be expected to detect.
(a) Find the power of this test against the alternative  = 299.
(b) Find the power against the alternative  = 295.
(c) Is the power against  = 290 higher or lower than the value
you found in (b)? Explain why
this result makes sense.
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Solution
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Exercise
• You have an SRS of size n = 9 from a normal distribution with
σ = 1. You wish to test the following
H0:  = 0
Ha:  > 0
• You decide to reject H0 if X  0 and to accept H0 otherwise.
(a) Find the probability of a Type I error, that is, the probability that
your test rejects H0 when in fact  = 0.
(b) Find the probability of a Type II error when  = 0.3. This is the
probability that your test accepts H0 when in fact  = 0.3.
(c) Find the probability of a Type II error when  = 1.
Answer: (a) P( X > 0 when  = 0) = P(Z > 0) = 0.50.
0  0.3
(b) P( X  0 when  = 0.3) = P(Z  1 / 9 ) = P(Z  –0.9) = 0.1841.
0 1
(c) P( X  0 when  = 1) = P(Z 
) = P(Z  –3) = 0.0013.
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Qestion17 Final Exam Dec 2000
When testing H0: μ = 5 vs Ha: μ ≠ 5 at  = 0.01 with n =40
suppose that the probability of a type II error () is equal to
0.02 when  = 2. Which of the following statements are true?
a)  > 0.02 when  = 3
b)  > 0.02 if the sample size was 50 (at  = 2)
c)  > 0.02 if  had been twice as large. (at  = 2)
d) The power of the test is at  = 2 is 0.99
Answer: a and c
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Exercise
A study was carried out to investigate the effectiveness of a
treatment. 1000 subjects participated in the study, with 500
being randomly assigned to the "treatment group" and the other
500 to the "control (or placebo) group". A statistically significant
difference was reported between the responses of the two groups
(P <0 .005).
State whether the following statements are true of false.
a) There is a large difference between the effects of the treatment
and the placebo.
b) There is strong evidence that the treatment is very effective.
c) There is strong evidence that there is some difference in effect
between the treatment and the placebo.
d) There is little evidence that the treatment has some effect.
e) The probability that the null hypothesis is true is less than 0.005.
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Use and abuse of Tests
• The spirit of a test of significance is to give a clear statement
of the degree of evidence provided by the sample against the
null hypothesis. The P-value does this. There is no sharp
evidence between “significant” and “not significant” only
increasingly strong evidence as the P-value decreases.
• When large samples are available, even tiny deviations from
the null hypothesis will be significant (small P-value).
Statistically significant effect need not be practically
important. Always plot the data and examine them carefully.
Beware of outliers.
• On the other hand, lack of significant does not imply that H0 is
true, especially when the test has low power. When planning a
study, verify that the test you plan to use does have high
probability of detecting an effect of the size you hope to find.
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• Significant tests are not always valid. Faulty data collection,
outliers in the data, and testing a hypothesis on the same data
that first suggested that hypothesis can invalidate a test. Many
tests run at once will probably produce some significant results
by chance alone, even if the null hypotheses are true.
• The reasoning behind statistical significance works well if you
decide what effect you are seeking, design an experiment or
sample to search for it, and use a test of significance to weight
the evidence you get.
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Example
Suppose that the population of scores of the high school
seniors that took the SAT-Verbal test this year follows a normal
distribution with  = 48 and  = 90. A report claims that
10,000 students who took part in the national program for
improving SAT-verbal scores had a significantly better score
(at the 5% level of sig.) than the population as a whole.
In order to determine if the improvement is of practical
significance one should:
 Find out the actual mean score for the 10,000 students.
 Fine out the actual p-value.
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Example 6.23 on page 396 in IPS
• Suppose that we are testing the hypothesis of no correlation
between two variables. With 400 observation, an observed
correlation of only r = 0.1 is significant evidence at the α =
0.05 level that the correlation in the population is not zero. The
low significance level does not mean there is strong
association, only that there is some evidence of some
association.
• This is an example where the test results are statistically
significant but not practically significant.
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