{"id":511,"date":"2023-02-27T17:30:47","date_gmt":"2023-02-27T17:30:47","guid":{"rendered":"https:\/\/content.one.lumenlearning.com\/introstatstest\/?post_type=chapter&p=511"},"modified":"2025-08-31T10:32:43","modified_gmt":"2025-08-31T10:32:43","slug":"sampling-methods-and-bias-learn-it-4","status":"publish","type":"chapter","link":"https:\/\/content.one.lumenlearning.com\/introstatstest\/chapter\/sampling-methods-and-bias-learn-it-4\/","title":{"raw":"Sampling Methods and Bias: Learn It 3","rendered":"Sampling Methods and Bias: Learn It 3"},"content":{"raw":"

Sampling Methods Summary<\/h2>\r\n

Let's sum up the sampling methods so far.<\/p>\r\n

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sampling methods<\/h3>\r\n

Simple random sampling<\/strong> assigns a number to every member of the population, then uses a random number generator to select a sample.<\/p>\r\n

Systematic sampling<\/strong> assigns a number to every member of the population, then choses individuals or entities from the population at regular intervals (e.g. every 4th individual from a randomly selected starting point).<\/p>\r\n

Stratified sampling<\/strong> divides a population into groups via some criterion, then uses simple random selection or systematic selection to collect a sample from each group.<\/p>\r\n

Cluster sampling<\/strong> divides a population into groups via some criterion, then uses simple random selection or systematic selection to select one or more groups as the sample.<\/p>\r\n

Convenience sampling<\/strong> selects a sample most accessible to the researcher.<\/p>\r\n<\/section>\r\n

A sampling method is\u00a0unbiased<\/strong>\u00a0if, on average, it results in a representative sample of the population. A sampling method is\u00a0biased<\/strong> if it has a tendency to produce samples that are not representative of the population. If the sampling method is biased, we cannot generalize our results to the population and can only make statements about the sample itself.<\/section>\r\n
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[ohm2_question hide_question_numbers=1]686[\/ohm2_question]<\/section>\r\n
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\"\"[choosedataset divId=\"tnh-choose-dataset\" title=\"Choose Your Own Dataset\" label=\"For this problem, you'll create and analyze different samples using a data set of your choosing.\" default=\"Choose a Dataset\"]
\r\n[datasetoption]
\r\n[displayname]500 Billionaires[\/displayname]
\r\n[ohmid]2645[\/ohmid]
\r\n[\/datasetoption][datasetoption]
\r\n[displayname]Traffic Stops in NYC[\/displayname]
\r\n[ohmid]2646[\/ohmid]
\r\n[\/datasetoption][datasetoption]
\r\n[displayname]Roller Coasters[\/displayname]
\r\n[ohmid]2647[\/ohmid]
\r\n[\/datasetoption][\/choosedataset]<\/section>\r\n<\/section>\r\n<\/section>\r\n<\/section>","rendered":"

Sampling Methods Summary<\/h2>\n

Let’s sum up the sampling methods so far.<\/p>\n

\n

sampling methods<\/h3>\n

Simple random sampling<\/strong> assigns a number to every member of the population, then uses a random number generator to select a sample.<\/p>\n

Systematic sampling<\/strong> assigns a number to every member of the population, then choses individuals or entities from the population at regular intervals (e.g. every 4th individual from a randomly selected starting point).<\/p>\n

Stratified sampling<\/strong> divides a population into groups via some criterion, then uses simple random selection or systematic selection to collect a sample from each group.<\/p>\n

Cluster sampling<\/strong> divides a population into groups via some criterion, then uses simple random selection or systematic selection to select one or more groups as the sample.<\/p>\n

Convenience sampling<\/strong> selects a sample most accessible to the researcher.<\/p>\n<\/section>\n

A sampling method is\u00a0unbiased<\/strong>\u00a0if, on average, it results in a representative sample of the population. A sampling method is\u00a0biased<\/strong> if it has a tendency to produce samples that are not representative of the population. If the sampling method is biased, we cannot generalize our results to the population and can only make statements about the sample itself.<\/section>\n
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