5-1 Biostatistics CHS 221 by Dr. WajedHatamleh lecture 7&8 Sampling &Sampling Distributions Dr. wajed Hatamleh

5-2 Learning Objectives Determine when to use sampling instead of a census. Distinguish between random and nonrandom sampling. Decide when and how to use various sampling techniques. Be aware of the different types of error that can occur in a study. Understand the impact of the Central Limit Theorem on statistical analysis. Use the sampling distributions of. x Dr. wajed Hatamleh

5-3 Reasons for Sampling Sampling can save money. Sampling can save time. For given resources, sampling can broaden the scope of the data set. Because the research process is sometimes destructive, the sample can save product. If accessing the population is impossible; sampling is the only option. Dr. wajed Hatamleh

5-4 Random Versus Nonrandom Sampling Random sampling Every unit of the population has the same probability of being included in the sample. A chance mechanism is used in the selection process. Eliminates bias in the selection process Also known as probability sampling Nonrandom Sampling Every unit of the population does not have the same probability of being included in the sample. Open the selection bias Not appropriate data collection methods for most statistical methods Also known as nonprobability sampling Dr. wajed Hatamleh

5-5 Random Sampling Techniques Simple Random Sample Stratified Random Sample Systematic Random Sample Cluster (or Area) Sampling Dr. wajed Hatamleh

5-6 Simple Random Sample Number each frame unit from 1 to N. Use a random number table or a random number generator to select n distinct numbers between 1 and N, inclusively. Easier to perform for small populations Dr. wajed Hatamleh

5-7 Simple Random Sample: Numbered Population Frame 01 Alaska Airlines 02 Alcoa 03 Ashland 04 Bank of America 05 BellSouth 06 Chevron 07 Citigroup 08 Clorox 09 Delta Air Lines 10 Disney 11 DuPont 12 Exxon Mobil 13 General Dynamics 14 General Electric 15 General Mills 16 Halliburton 17 IBM 18 Kellog 19 KMart 20 Lowe’s 21 Lucent 22 Mattel 23 Mead 24 Microsoft 25 Occidental Petroleum 26 JCPenney 27 Procter & Gamble 28 Ryder 29 Sears 30 Time Warner Dr. wajed Hatamleh

5-8 Simple Random Sampling: Random Number Table N = 30 n = 6 Dr. wajed Hatamleh

5-9 Simple Random Sample: Sample Members 01 Alaska Airlines 02 Alcoa 03 Ashland 04 Bank of America 05 BellSouth 06 Chevron 07 Citigroup 08 Clorox 09 Delta Air Lines 10 Disney 11 DuPont 12 Exxon Mobil 13 General Dynamics 14 General Electric 15 General Mills 16 Halliburton 17 IBM 18 Kellog 19 KMart 20 Lowe’s 21 Lucent 22 Mattel 23 Mead 24 Microsoft 25 Occidental Petroleum 26 JCPenney 27 Procter & Gamble 28 Ryder 29 Sears 30 Time Warner N = 30 n = 6 Dr. wajed Hatamleh

5-10 Stratified Random Sample Population is divided into nonoverlapping subpopulations called strata A random sample is selected from each stratum Potential for reducing sampling error Dr. wajed Hatamleh

5-11 Stratified Random Sample: Population of FM Radio Listeners years old (homogeneous within) (alike) years old (homogeneous within) (alike) years old (homogeneous within) (alike) Hetergeneous (different) between Hetergeneous (different) between Stratified by Age Dr. wajed Hatamleh

5-12 Nonrandom Sampling Convenience Sampling: sample elements are selected for the convenience of the researcher Judgment Sampling: sample elements are selected by the judgment of the researcher Snowball Sampling: survey subjects are selected based on referral from other survey respondents Dr. wajed Hatamleh

5-13 Errors u Data from nonrandom samples are not appropriate for analysis by inferential statistical methods. u Sampling Error occurs when the sample is not representative of the population u Nonsampling Errors Missing Data, Recording, Data Entry, and Analysis Errors Poorly conceived concepts, unclear definitions, and defective questionnaires Response errors occur when people so not know, will not say, or overstate in their answers Dr. wajed Hatamleh

5-14 Sampling Distribution of Proper analysis and interpretation of a sample statistic requires knowledge of its distribution. Process of Inferential Statistics x Dr. wajed Hatamleh

5-15 Distribution of a Small Finite Population Population Histogram Frequency N = 8 54, 55, 59, 63, 68, 69, 70 Dr. wajed Hatamleh

5-16 Sample Space for n = 2 with Replacement SampleMeanSampleMeanSampleMeanSampleMean 1(54,54)54.017(59,54)56.533(64,54)59.049(69,54)61.5 2(54,55)54.518(59,55)57.034(64,55)59.550(69,55)62.0 3(54,59)56.519(59,59)59.035(64,59)61.551(69,59)64.0 4(54,63)58.520(59,63)61.036(64,63)63.552(69,63)66.0 5(54,64)59.021(59,64)61.537(64,64)64.053(69,64)66.5 6(54,68)61.022(59,68)63.538(64,68)66.054(69,68)68.5 7(54,69)61.523(59,69)64.039(64,69)66.555(69,69)69.0 8(54,70)62.024(59,70)64.540(64,70)67.056(69,70)69.5 9(55,54)54.525(63,54)58.541(68,54)61.057(70,54) (55,55)55.026(63,55)59.042(68,55)61.558(70,55) (55,59)57.027(63,59)61.043(68,59)63.559(70,59) (55,63)59.028(63,63)63.044(68,63)65.560(70,63) (55,64)59.529(63,64)63.545(68,64)66.061(70,64) (55,68)61.530(63,68)65.546(68,68)68.062(70,68) (55,69)62.031(63,69)66.047(68,69)68.563(70,69) (55,70)62.532(63,70)66.548(68,70)69.064(70,70)70.0 Dr. wajed Hatamleh

5-17 Distribution of the Sample Means Sampling Distribution Histogram Frequency Dr. wajed Hatamleh

5-18 Central Limit Theorem For sufficiently large sample sizes (n  30), the distribution of sample means, is approximately normal; the mean of this distribution is equal to , the population mean; and its standard deviation is it is a standard error (SE), regardless of the shape of the population distribution. x n  Dr. wajed Hatamleh

5-19 Central Limit Theorem Dr. wajed Hatamleh

5-20 Exponential Population n = 2n = 5n = 30 Distribution of Sample Means for Various Sample Sizes Uniform Population n = 2n = 5n = 30 Dr. wajed Hatamleh

5-21 Sampling from a Normal Population The distribution of sample means is normal for any sample size. Dr. wajed Hatamleh

5-22 Z Formula for Sample Means Dr. wajed Hatamleh

Given the population of men has normally distributed weights with a mean of 172 lb and a standard deviation of 29 lb, a) if one man is randomly selected, find the probability that his weight is greater than 175 lb. b) if 20 different men are randomly selected, find the probability that their mean weight is greater than 175 lb (so that their total weight exceeds the safe capacity of 3500 pounds). Example – Water Taxi Safety

z = 175 – 172 = a) if one man is randomly selected, find the probability that his weight is greater than 175 lb. Example – cont

b) if 20 different men are randomly selected, find the probability that their mean weight is greater than 172 lb. Example – cont z = 175 – 172 =

b) if 20 different men are randomly selected, their mean weight is greater than 175 lb. P(x > 175) = It is much easier for an individual to deviate from the mean than it is for a group of 20 to deviate from the mean. a) if one man is randomly selected, find the probability that his weight is greater than 175 lb. P(x > 175) = Example - cont