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Do Now : How do you think you collect data?

AP Statistics C5 D2 HW : p.287 #25 – 30 Obj : to understand types of samples and possible errors. Do Now : How do you think you collect data?. Sampling Designs.

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Do Now : How do you think you collect data?

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  1. AP Statistics C5 D2HW: p.287 #25 – 30Obj: to understand types of samples and possible errors Do Now: How do you think you collect data?

  2. Sampling Designs • SRS (ensure that each individual has an equal chance of being selected for the sample AND that each subset has an equal chance of being the sample) • Convenience sample • Quota sampling -ensure that you have a certain # from each group of interest within the population -Ex: If you have a class that is 30% girls and 70% boys, you may want to choose a sample of size 10 that includes 3 girls and 7 boys.

  3. Probability Samples • SRS is one type – each element has an equal probability of being selected • Stratified Random Sample - the population is divided into homogeneous groups (ex: urban, suburban, rural) called strata - get an SRS from each strata, then put all SRS together to form a sample - this ensures that all groups within a population are represented

  4. Multistage Cluster Sample Ex: Suppose we want a sample of US households’ weekly spending on groceries. It would be a lot of work and cost a lot of money to take an SRS of households across the country. One day you might have to go to a house in Cleveland and the next day you have to go to New York, etc.

  5. Instead you could take a multistage cluster sample: • Take an SRS of states in the US. • Take an SRS of towns within the states selected in stage 1. • Take an SRS of the streets in the towns selected in stage 2. • Take an SRS of the houses on the streets selected in stage 4.

  6. This way, you end up interviewing 20 households on one block instead of 1 households on 20 blocks Multistage cluster sampling can be very efficient and cost effective while making sure that your sample is still randomly selected.

  7. Systematic Random Sample - Survey every 50th person who walks by.

  8. Errors • Sample frame error - when sample frame (list of possible subjects who could be selected in a sample) does not represent the population. • Random sample error – chance variation (sample of students from this school just happens to contain only boys) • Sampling method error – choosing the wrong method (convenience sampling)

  9. Errors • Response bias – wording of questions, order of answer choices, behavior of interviewer, dishonesty in responses • Sample size is too small – larger samples give more accurate results

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