Confidence intervals and hypothesis testing

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Transcript of Confidence intervals and hypothesis testing
Confidence intervals and hypothesis testing
Petter Mostad
2005.10.03
Confidence intervals (repetition)
• Assume μ and σ2 are some real numbers, and assume the data X1,X2,…,Xn are a random sample from N(μ,σ2). – Then
– thus
– so
and we say that is a confidence interval for μ with 95% confidence, based on the statistic
~ (0,1)/
XZ N
n
( 1.96 1.96) 95%P Z
( 1.96 1.96 ) 95%n n
P X X ( 1.96 , 1.96 )
n nX X
X
Confidence intervals, general idea
• We have a model with an unknown parameter• We find a ”statistic” (function of the sample) with
a known distribution, depending only on the unknown parameter
• This distribution is used to construct an interval with the following property: If you repeat many times selecting a parameter and simulating the statistic, then about (say) 95% of the time, the confidence interval will contain the parameter
Hypothesis testing
• Selecting the most plausible model for the data, among those suggested
• Example: Assume X1,X2,…,Xn is a random sample from N(μ,σ2), where σ2 is known, but μ is not; we want to select μ fitting the data.
• One possibility is to look at the probability of observing the data given different values for μ. (We will return to this)
• Another is to do a hypothesis test
Example
• We select two alternative hypotheses: – H0: – H1:
• Use the value of to test H0 versus H1: If is far from , it will indicate H1.
• Under H0, we know that
• Reject H0 if is outside
0( 1.96 1.96 ) 95%n n
P X X
0 0( 1.96 , 1.96 )n n
X X
X
0
0
0
General outline for hypothesis testing
• The possible hypotheses are divided into H0, the null hypothesis, and H1, the alternative hypothesis
• A hypothesis can be– Simple, so that it is possible to compute the
probability of data (e.g., )– Composite, i.e., a collection of simple
hypotheses (e.g., )
3.7
3.7
General outline (cont.)
• A test statistic is selected. It must: – Have a higher probability for ”extreme” values under
H1 than under H0
– Have a known distribution under H0 (when simple)• If the value of the test statistic is ”too extreme”,
then H0 is rejected. • The probability, under H0, of observing the given
data or something more extreme is called the pvalue. Thus we reject H0 if the pvalue is small.
• The value at which we reject H0 is called the significance level.
Note:
• There is an asymmetry between H0 and H1: In fact, if the data is inconclusive, we end up not rejecting H0.
• If H0 is true the probability to reject H0 is (say) 5%. That DOES NOT MEAN we are 95% certain that H0 is true!
• How much evidence we have for choosing H1 over H0 depends entirely on how much more probable rejection is if H1 is true.
Errors of types I and II
• The above can be seen as a decision rule for H0 or H1.
• For any such rule we can compute (if both H0 and H1 are simple hypotheses):
P(accept  H0) P(accept  H1)
P(reject  H0) P(reject  H1)
Accept H0
Reject H0
H0 true H1 true
TYPE II error
TYPE I errorSignificance
1  power
Significance and power
• If H0 is composite, we compute the significance from the simple hypothesis that gives the largest probability of rejecting H0.
• If H1 is composite, we compute a power value for each simple hypothesis. Thus we get a power function.
Example 1: Normal distribution with unknown variance
• Assume
• Then
• Thus
• So a confidence interval for , with significance is given by
21 2, ,..., ~ ( , )nX X X N
1~/
n
Xt
s n
1, / 2 1, / 2( )s sn nn n
P X t X t
1, / 2 1, / 2( , )s sn nn n
X t X t
Example 1 (Hypothesis testing)
• Hypotheses:
• Test statistic under H0
• Reject H0 if or if
• Alternatively, the pvalue for the test can be computed (if ) as the such that
0 0:H
01~
/n
Xt
s n
0 1, / 2s
n nX t
20 1, / 2n n
X t
1 0:H
0 1, / 2s
n nX t
0X
Example 1 (cont.)
• Hypotheses:
• Test statistic assuming
• Reject H0 if
• Alternatively, the pvalue for the test can be computed as the such that
0 0:H
01~
/n
Xt
s n
0 1,s
n nX t
20 1,n n
X t
1 0:H
0
Example 1 (cont.)
• Assume that you want to analyze as above the data in some column of an SPSS table.
• Use ”Analyze” => ”Compare means” => ”Onesample T Test”
• You get as output a confidence interval, and a test as the one described above.
• You may adjust the confidence level using ”Options…”
Example 2: Differences between means
• Assume and
• We would like to study the difference• Four different cases:
– Matched pairs– Known population variances– Unknown but equal population variances– Unknown and possibly different pop. variances
21 2, ,..., ~ ( , )n x xX X X N
21 2, ,..., ~ ( , )m y yY Y Y N
1 2
Known population variances
• We get
• Confidence interval for 1 2
22
( )~ (0,1)x y
yx
x y
X YN
n n
22
/ 2yx
x y
X Y Zn n
Unknown but equal population variances
• We get
where
• Confidence interval for
22 2
( )~
x y
x yn n
p p
x y
X Yt
s s
n n
2 22 ( 1) ( 1)
2x x y y
px y
n s n ss
n n
x y
2 2
2, / 2x y
p pn n
x y
s sX Y t
n n
Hypothesis testing: Unknown but equal population variances
• Hypotheses:
• Test statistic:
• Reject H0 if or if
0 : x yH 1 : x yH
22 2~
x yn n
p p
x y
X Yt
s s
n n
2, / 22 2 x yn n
p p
x y
X Yt
s s
n n
2, / 22 2 x yn n
p p
x y
X Yt
s s
n n
”T test with equal variances”
Unknown and possibly unequal population variances
• We get
where
• Conf. interval for
22
( )~x y
yx
x y
X Yt
ss
n n
22 2
2 22 2 ( / )( / )
1 1
yx
x y
ssn n
y yx x
x y
s ns n
n n
x y 22
, / 2yx
x y
ssX Y t
n n
Hypothesis test: Unknown and possibly unequal pop. variances
• Hypotheses:
• Test statistic
• Reject H0 if or if
0 : x yH 1 : x yH
22~
yx
x y
X Yt
ssn n
, / 222yx
x y
X Yt
ssn n
, / 222
yx
x y
X Yt
ss
n n
”T test with unequal variances”
Practical examples:
• The lengths of children in a class are measured at age 8 and at age 10. Use the data to find an estimate, with confidence limits, on how much children grow between these ages.
• You want to determine whether a costly operation is generally done more cheaply in France than in Norway. Your data is the actual costs of 10 such operations in Norway and 20 in France.
Example 3: Population proportions
• Assume , so that is a frequency.• Then
• Thus
• Thus
• Confidence interval for
~ ( , )X Bin n Xnp
~ (0,1)(1 ) /
pN
n
~ (0,1)(1 ) /
pN
p p n
(approximately, for large n)
(approximately, for large n)
/ 2 / 2
(1 ) (1 )p p p pP p Z p Z
n n
/ 2 / 2
(1 ) (1 ),
p p p pp Z p Z
n n
Example 3 (Hypothesis testing)
• Hypotheses:
• Test statistic under H0, for large n
• Reject H0 if or if
0 0:H
0
0 0
~ (0,1)(1 ) /
pN
n
0 00 / 2
(1 )p Z
n
1 0:H
0 00 / 2
(1 )p Z
n
Example 4: Differences between population proportions
• Assume and , so that and are frequencies
• Then
• Confidence interval for
1 1 1~ ( , )X Bin n1
11Xnp
1 2 1 2
1 1 2 2
1 2
( )~ (0,1)
(1 ) (1 )
p pN
n n
(approximately)
2 2 2~ ( , )X Bin n2
22Xnp
1 2
1 1 2 21 2 / 2
1 2
(1 ) (1 )p p p pp p Z
n n
Example 4 (Hypothesis testing)
• Hypotheses:
• Test statistic
where
• Reject H0 if
0 1 2:H 1 2
0 0 0 0
1 2
~ (0,1)(1 ) (1 )
p pN
p p p pn n
1 1 2:H
1 1 2 20
1 2
n p n pp
n n
1 2/ 2
0 0 0 0
1 2
(1 ) (1 )
p pZ
p p p pn n
Example 5: The variance of a normal distribution
• Assume
• Then
• Thus
• Confidence interval for
21 2, ,..., ~ ( , )nX X X N
22
12
( 1)~ n
n s
2 2
2 21,1 / 2 1, / 2
( 1) ( 1),
n n
n s n s
22 2
1,1 / 2 1, / 22
( 1)n n
n sP
2
Example 6: Comparing variances for normal distributions
• Assume• We get
• Fnx1,ny1 is an F distribution with nx1 and ny1 degrees of freedom
• We can use this exactly as before to obtain a confidence interval for and for testing for example if
• Note: The assumption of normality is crucial!
21 2, ,..., ~ ( , )n x xX X X N 2
1 2, ,..., ~ ( , )m y yY Y Y N 2 2
1, 12 2
/~
/ x y
x xn n
y y
sF
s
2 2/x y 2 2x y
Sample size computations
• For a sample from a normal population with known variance, the size of the conficence interval for the mean depends only on the sample size.
• So we can compute the necessary sample size to match a required accuracy
• Note: If the variance is unknown, it must somehow be estimated on beforehand to do the computation
• Works also for population proportion estimation, giving an inequality for the required sample size
Power computations
• If you reject H0, you know very little about the evidence for H1 versus H0 unless you study the power of the test.
• The power is 1 minus the probability of rejecting H0 given that a hypothesis in H1 is true.
• Thus it is a function of the possible hypotheses in H1.
• We would like our tests to have as high power as possible.