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Presentation is loading. Please wait. © 2011 1. Sampling A sample is a subset of the population In a sample, you study a few members of the population In a census, you study.

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Presentation on theme: " © 2011 1. Sampling A sample is a subset of the population In a sample, you study a few members of the population In a census, you study."— Presentation transcript:

1 © 2011 1

2 Sampling A sample is a subset of the population In a sample, you study a few members of the population In a census, you study every member of the population If done properly, a sample can be accurate and avoid the cost and time needed for a full census Sampling error occurs when the sample is not representative of the population. Every sample will be a bit different from the population, and every sample will have some amount of sampling error Sampling error could be due to bias Bias by including members outside your population Bias by including too few or too many members who do not match the full variability of the general population properly © 20112

3 You need to sample enough members to have the full range of opinion Is your sample size large enough? One student who drives too fast is not a big enough sample to say all students drive too fast A sample of 2 million is too large By the time you call 2 million people, you’ll be 69 years old If 7 friends speed, it is not a statistically significant proof. It is called anecdotal evidence and is almost worthless. You need to calculate the size of sample needed to have statistically significant proof The more variety in the population, the larger the sample needed to be representative of the whole population The more confidence you need, the larger the sample size needed. The more error you can tolerate, the smaller your sample can be. © 20133

4 The sample must be representative of population If I only talk to members of the sports car club, they are not representative of the whole college, so the sample will be biased To avoid sampling error, you need a method that avoids selecting samples that do not match the population well. Example: Simple random sample method There are some famous examples of biased studies Literary Digest Radio phoning, texting, magazine surveys are usually wrong Common source of sampling error today is non-response People won’t do a survey, or skip the important questions Tip: keep your survey short © 20114

5 Methods to avoid sample bias Simple random sampling Most trusted method. Give every member of the population a number Draw random numbers to see who you should sample Put numbers in a hat, or use the Excel random number generator With this method, you do not have any control over who is included Stratified Random sample Similar to simple random but you ensure you get enough people from important groups. Example: you want equal numbers of men and women, children and adults, … Systematic Random You pick every n members. Perhaps the best students sit in the front, so to ensure you do not randomly select too many students in front, you pick every 5 th student. © 20115

6 Some poor methods of sampling Convenience – pick people who are easy to reach Judgement – some researchers think they know who the best candidates should be research shows expert judgment is almost never as good as simple random sampling Snowball – nice sounding word even though the method is poor. In this method, you have one participant recommend other participants Given the choice, always use random versus non- random sampling methods. © 20116

7 Sampling Frame Pick your population, but how do you reach them? Phone book, membership list, organizational records, … The method used to find members of the population is the sampling frame Whatever sampling frame you use, you may find that the frame includes some people it should not the frame does not include some people it should Example: Toronto phone book frame will introduce sampling errors. Phone book includes people who have left Toronto, and it does not include some people who just moved to Toronto. © 20117

8 Non-sampling errors There are errors that have nothing to do with your sample called non-sampling errors. Assuming your sample was good, other errors are…. Missing data is a common issue Incorrectly recorded data Words were misunderstood Survey questions had errors Example of a common error is double-barrelled questions Would you like to die or pay more taxes? If 100% said ‘yes’, have we proven people want to pay more tax? © 20118

9 Ethics Board Most organizations have an Ethics Board that must review and approve research. Was data collected fairly and accurately? How long will data be kept? Who will see client data? Was confidentiality respected? Could participants be harmed in the study? Are all participants fully informed of their rights? Before you do research, check what approvals are needed by your organization. If you are invited to participate in a study, understand you can refuse any question, quit at any time, and ask any question you like about how the data will be used. © 20139

10 The end A sample and statistics survey is easy and cheap to do Can show valuable insights Increasingly, quantitative studies are growing in importance as the amount of data in society grows exponentially © 201110

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