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Sampling

Sampling. Mathsfest 2014. Why Sample?. Jan8, 2003 Air Midwest Flight 5481 from Douglas International Airport in North Carolina stalled after take off, crashed into a hangar and burst into flames. All 21 people on board perished.

garrett-tillman
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Sampling

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  1. Sampling Mathsfest 2014

  2. Why Sample? Jan8, 2003 Air Midwest Flight 5481 from Douglas International Airport in North Carolina stalled after take off, crashed into a hangar and burst into flames. All 21 people on board perished. A subsequent investigation revealed that the weight of the passengers was a factor that contributed to the crash. This prompted the FAA to collect weight information from randomly selected flights so that old assumptions about passenger weights could be updated.

  3. Sampling Distributions Suppose that we draw all possible samples of size n from a given population. Suppose further that we compute a statistic (e.g., a mean) for each sample. The probability distribution of this statistic is called a sampling distribution

  4. Sampling With Replacement • When selecting a relatively small sample from a large population, it makes no significant difference whether we sample with replacement or without replacement. • Sampling with replacement results in independent events that are unaffected by previous outcomes. Independent events are easier to analyse and result in simpler formulas.

  5. Simple Random Sample

  6. Advantages of Simple Random Sampling • Every member of the population has an equal chance of being represented in the sample • The simple random sample should be representative of the population. Theoretically the only thing that can compromise its representativeness is luck • If the sample is not representative of the population, then the random variation is called sampling error

  7. Disadvantages of Simple Random Sampling • A complete and up to date list of all the population is required • Such a list is usually not available for large populations

  8. Estimators The sample statistic targets the population parameter

  9. Estimators The sample statistic does not target the population parameter

  10. Stratified Random Sampling

  11. Advantages of Stratified Random Sampling • Provides greater precision than a simple random sample of the same size • Smaller samples are required, thereby saving money • Can guard against an unrepresentative sample

  12. Disadvantages of Stratified Random Sampling May require more administrative effort than a simple random sample A complete and up to date list of the population is required

  13. Uniform Population Distribution 6 5 frequency 4 3 2 1 raw score 8 7 9 1 2 3 4 5 6 What is the mean of this population? 5 What is the standard deviation of this population? = 2.24

  14. Distribution of Sample Means: Samples of Size 2 Sample Scores Mean ( )

  15. Distribution of Sample Means from Samples of Size n = 2 6 5 frequency 4 3 2 1 8 7 9 1 2 3 4 5 6 sample mean

  16. Distribution of Sample Means from Samples of Size n = 2 6 5 frequency 4 3 2 1 8 7 9 1 2 3 4 5 6 sample mean p( > 7) = ?

  17. Distribution of Sample Means from Samples of Size n = 2 6 5 frequency 4 3 2 1 8 7 9 1 2 3 4 5 6 sample mean P( > 7) = = 6 %

  18. Distribution of Sample Means 6 5 frequency 4 Distribution of Sample Means Population Distribution 3 2 6 1 5 raw score frequency 8 7 9 1 2 3 4 5 6 4 3 2 1 8 7 1 2 3 4 5 6 9 sample mean P(X > 7) = 25%

  19. Cluster Sampling

  20. Advantages of Cluster Sampling • Inexpensive • Limited resources can be allocated to a few randomly selected clusters. • Easy to implement • Subjects are easily accessed

  21. Disadvantages of Cluster Sampling • From all the different probability sampling methods, this technique is the least representative of the population. • There is a tendency for individuals within a cluster to have similar characteristics, therefore there is a chance that a researcher may have an over represented or under represented cluster.

  22. Mean of Sample Means Standard Deviation of Population= Mean of Population = 5

  23. Skewed Population Distribution 6 5 frequency 4 3 2 1 raw score 8 7 9 1 2 3 4 5 6

  24. Distribution of Sample Means Samples of Size 2 12 10 ` 8 frequency 6 4 2 2 4 3 5 6 sample mean Spreadsheet

  25. Systematic Random Sample

  26. Advantages of Systematic Random Sampling • Representative of the population • Because the sample is random, we can make statistical conclusions that would be considered valid

  27. Disadvantages of Systematic Random Sampling • A complete and up to date list of all the population is required • If the population is listed in some standardised pattern, then systematic sampling could pick out similar members rather than completely random members

  28. Uniform Population Distribution 6 5 frequency 4 3 2 raw score 1 6 8 7 1 2 3 4 5 Mean = 5 Standard deviation = = 2.24

  29. Distribution of Sample Means Sample Size 3 24 22 20 18 frequency 16 14 12 10 8 6 4 2 8 7 9 Spreadsheet 1 2 3 4 5 6 sample mean

  30. Things to Notice The sample means tend to pile up around the population mean. The distribution of sample means is approximately normal in shape, even though the population distribution was not. The distribution of sample means has less variability than does the population distribution. Increasing sample size decreases the variability in the distribution of samples.

  31. The Central Limit Theorem TheCentral Limit Theorem states The sampling distribution of any statistic will be normal or nearly normal, if the sample size is large enough. The mean of the sampling distribution is the equal to the mean of the population. The standard deviation of the sampling distribution (also known as the standard error is the standard deviation of the population divided by the square root of the sample size

  32. Non Probabilistic Sampling • Quota Sampling • Convenience Sampling • Snowball Sampling

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