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Lying through Inference. Significance Tests and Confidence Intervals.

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Lying through inference

Lying through Inference

Significance Tests and

Confidence Intervals

Remember from yesterday: An online magazine bragged that 64% of Americans still buy CDs (Taken from an SRS of 1,000). In 2000, it was said that ¾ of all Americans bought CDs. Does this mean the proportion of CD buyers has changed?

p = .75 (75% buy)

p .75 (Some other proportion buys CDs)

Step 3: Find the p-value.

p-value = .011, α=.05

Since .011 < .05, there is sufficient evidence to reject the Null Hyp. We can conclude that the proportion of people who buy CDs has changed.

Ex 1: A local news station wanted to know the public’s opinion about the recent earth quakes in CA, and how many of the local Californians were ready for “The Big One.” During their morning news, they asked people to call in and state whether or not they had prepared a disaster kit for their home or car. Out of the 782 people who called in, 601 said that they had a kit prepared. Does this mean that most Californians are prepared in case of a large earthquake?

Is this sample reliable? Why or why not?

Bad sampling methods
Bad Sampling methods

Convenience Sampling: This type of sampling only selects those in a certain location, which ignores the individuals in other areas.

Voluntary Response: This only samples those who strongly agree or disagree with the questions being posed (like a phone in survey on TV).

Undercoverage: This sampling leaves out entire groups in a population.

Nonresponse: This is when many who are asked to participate in a survey or questionnaire chose not to, leaving their opinions out of the data collected.