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Unformatted text preview: e get a pvalue 2 × P (Z > z = 0.33) = 2 × 0.3707 = 0.7414.
This value is bigger than any reasonable α so reach at the same conclusion as before. We
fail to reject the null hypothesis.
Let’s go back one slide and see this on a picture. Utku Suleymanoglu (UMich) Hypothesis Testing 25 / 37 Testing Hypothesis about the Population Mean: σ known Hypothesis Testing Fundamentals Recap
Before we go on to diﬀerent cases, let’s repeat the general idea of hypothesis testing:
We have an hypothetical value for a population parameter (µ = µ0 ) as a claim and
we want to test this.
We have a sample and a point estimate x = 2, let’s say.
¯
We know the sampling distribution of x assuming the claim is true from chapter 6.
¯
Then we can evaluate the probability of x or a similar draw from this sampling
¯
distribution.
To do that we need to transform our normal random variable to standard normal,
this gives us zstatistic.
Then we can either
calculate the probability associated with the zstatistic and see if it is small or big
(pvalue approach)
compare it to some critical zvalue so that we can assess how far oﬀ it is from the
claimed value. (critical value approach) Either way, based on the assumption the claim is true, we assess the correctness of
the claim by comparing it to what we observe in the data.
If two are “diﬀerent enough”, we say the claim is (probably) not correct.
Utku Suleymanoglu (UMich) Hypothesis Testing 26 / 37 σ not Known Case 2: σ not known, population normal When σ is not known, we can use s , sample standard deviation, instead. Just like we did
before. . . for CI’s.
But remember, we need a modiﬁcation to make this work. We need to use tdistribution
instead of standard normal distribution.
pvalue approach is hard to perform with tdistribution, so we will just use the critical
value approach.
Let’s do a one tailed example ﬁrst . . . Utku Suleymanoglu (UMich) Hypothesis Testing 27 / 37 σ not Known OneTailed tTests
For one tailed tests involving hypotheses:
Left tailed:
H 0 : µ ≥ µ0
H1 :µ < µ0
Right tailed:
H 0 : µ ≤ µ0
H1 :µ > µ0
We reject the null the hypothesis if test statistic:
t= x − µ0
¯
√
s/ n is such that
t < −tα,n−1 for lefttailed tests
t > tα,n−1 for righttailed tests
where tα is the critical value with probability α in the upper tail.
Utku Suleymanoglu (UMich) Hypothesis Testing 28 / 37 σ not Known Example Suppose you are interested in the labor supply of elderly. You have a data set that
consists of 900 people aged 65. They are measu...
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 Spring '08
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