Random sampling is a crucial research technique that provides every individual in a population an equal chance of being selected for a study. It's key for creating representative samples, minimizing bias, and accurately reflecting population characteristics. The text explores different methods like simple, systematic, stratified, and cluster sampling, and discusses variables, sample size, and sampling variability in the context of research methodology.
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Random sampling is essential in research to ensure a representative sample that accurately reflects the characteristics of the entire population
Random sampling helps to mitigate biases and collect data that accurately represents the population
Researchers can use various methods such as simple, systematic, stratified, and cluster sampling to ensure a representative sample
Involves randomly selecting individuals from the entire population
Selects subjects at fixed intervals from an ordered list
Divides the population into strata based on characteristics and randomly samples from each stratum
Randomly selects entire groups or clusters from a divided population
Stratified sampling is particularly useful for ensuring representation across different population segments and producing more accurate and generalizable study results
Stratified random sampling involves dividing the population into strata based on specific characteristics to ensure representation across different population segments
Stratified random sampling leads to more accurate and generalizable study results compared to simple random sampling, especially when dealing with diverse populations
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To accurately reflect the traits of all college women in a study on depression, researchers may ______ select a sample of ______ from the total population.
randomly
1,000
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Effect of non-random sampling on study results
Leads to non-representative samples, potentially causing misleading conclusions.
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Consequence of selection bias in sampling
Results in data that does not accurately reflect the entire population's characteristics.
