Notes pg106-119

Notes pg106-119 - 1 Random Sampling In general, when we...

Info iconThis preview shows pages 1–4. Sign up to view the full content.

View Full Document Right Arrow Icon
P(X) C2 A1 X 1 2 3 4 E4 B2 D3 P(X) X 1.5 2.0 2.5 3.0 3.5 Step 4. nd our knowledge about ’s sampling distribution to draw a conclusion about μ Step 3. Step 1. eed to know about some population parameter, let’s say μ Compute from the sample to estimate μ Step 2. Take a random sample from the population Random Sampling In general, when we take a sample from a population we do it to estimate some characteristic (i.e. parameter) of a population with an appropriate statistic calculated from the sample. Of course, we want our sample to be representative of the population from which it was taken. In other words, we want our sample to be unbiased . If we want our sample to be unbiased and to be able to use our sample data to draw valid conclusions about our population, we need to employ random sampling . Simple random sampling is a method of sampling designed so that every item in the population has an equally likely chance of being selected. Problems that arise when sampling. 1. Selection bias—over- or under-representing some segment of the population. For example, if you want to estimate the proportion of registered voters in the U.S. who plan on voting Republican in the next presidential election, you would not want to draw your sample exclusively from South Carolina. 1
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
2. Non-response bias. When sampling by means of a survey, you have to remember that not everyone responds. How do we know that the people who respond to the survey are the same as those who do not? For example, if I sent our a survey asking college students about their drug and alcohol use, do you think that the people who send the survey back have the same usage as the ones who do not? 3.Measurement error. Could be lots of things. Can result from something as simple as inaccuracy in recording responses. Or can result from poorly worded questions, an interviewer’s effect on the respondent, or the effort made by the respondent. 4. Sampling error. Sampling error is the difference between the estimate of the population parameter obtained from the sample (i.e., the statistic) and the true value (i.e., the parameter). Sampling error can be measured and can be reduced by increasing the sample size. 2
Background image of page 2
Using the correct operational definition for the variables you are interested in can reduce problems with measurement error. For example, if you want to estimate the average square footage of homes in a particular suburb, be sure to specify whether you want “heated” or “unheated” square feet when phrasing the question to homeowners. Also, be
Background image of page 3

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Image of page 4
This is the end of the preview. Sign up to access the rest of the document.

Page1 / 17

Notes pg106-119 - 1 Random Sampling In general, when we...

This preview shows document pages 1 - 4. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online