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Questions and Answers
What is a biased selection?
What is a biased selection?
What is correspondence?
What is correspondence?
The defined relationship between the population sampled and a random-number table.
What are discards in sampling?
What are discards in sampling?
Unusable numbers generated from a random-number selection deviation.
What is a haphazard sample?
What is a haphazard sample?
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What does the term population refer to in sampling?
What does the term population refer to in sampling?
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What is probability-proportional-to-size sampling?
What is probability-proportional-to-size sampling?
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What is a random-number table?
What is a random-number table?
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What defines a random sample?
What defines a random sample?
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What is the random-stab method?
What is the random-stab method?
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What is random systemic sampling?
What is random systemic sampling?
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Define a representative sample.
Define a representative sample.
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What is meant by route in sampling?
What is meant by route in sampling?
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For a test of cash disbursements, the individual check number could be a ________ _____.
For a test of cash disbursements, the individual check number could be a ________ _____.
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What is sampling with replacement?
What is sampling with replacement?
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What is sampling without replacement?
What is sampling without replacement?
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Define stratified selection.
Define stratified selection.
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What is systematic sampling?
What is systematic sampling?
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Study Notes
Key Sampling Concepts
- Biased selection: Refers to a process that inhibits random selection, resulting in an unrepresentative sample.
- Correspondence: The established relationship between the sampled population and a random-number table, crucial for valid sampling.
- Discards: Unusable or irrelevant numbers produced during a random-number selection process.
- Haphazard sample: A sampling method lacking bias, intended to reflect the population accurately without systematic influence.
Definitions of Sampling Techniques
- Population: The complete set of items or individuals under study in an audit, also known as the universe or field.
- Probability-proportional-to-size sampling: A technique where each population unit's selection probability correlates with its recorded amount, often called dollar unit sampling.
- Random-number table: A resource consisting of randomly generated digits from 0 to 9, helpful for creating random samples.
- Random sample: A sample drawn randomly where every element has an equal chance of selection, ensuring a valid statistical representation, also termed probability sampling.
Specialized Sampling Methods
- Random-stab method: An approach to sample selection involving randomly opening a random-number table and using a random point to determine which row, column, and digit to start with.
- Random systemic sampling: A method that employs a variable sampling interval instead of a constant one, introducing randomness into systematic sampling.
- Representative sample: A sample that possesses key characteristics mirroring the population, typically achieved through random or haphazard selection methods.
Other Relevant Concepts
- Route: The predetermined sequence followed in a random-number table during sample selection to ensure randomness.
- Sampling frame: A comprehensive list or representation of sampling units, such as individual check numbers in cash disbursement tests.
- Sampling with replacement: A technique allowing the same item in a population to be reselected for the sample, increasing the likelihood of certain items being counted multiple times.
- Sampling without replacement: A method where selected items cannot be chosen again, commonly preferred in auditing practices.
- Stratified selection: A technique that involves categorizing sampling units into distinct groups to minimize variability and enhance accuracy of the sample.
- Systematic sampling: A sampling strategy where a starting point is chosen randomly, followed by selection of every nth item from the population.
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Description
Dive into the key terms from Chapter 2: Selecting a Representative Sample. This quiz helps reinforce your understanding of concepts like biased selection, correspondence, and discards, essential for grasping statistical sampling techniques.