Term
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Definition
a sample in which the items have unknown
probabilities of being selected.
Example: Convenience Sample and judgement sampling |
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Term
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Definition
sample
in which items are selected based upon known probabilities.
Example: Simple Random Sample, systematic, stratified, and cluster |
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Term
making
conclusions about the population,
that is, for statistical inference. |
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Definition
****Probability sampling is
NECESSARY when |
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Term
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Definition
the error
incurred by taking a sample
instead of a census. (Remember
as soon as you look at anything
less than the entire population,
you are not going to report with
100% accuracy the
characteristics of the population). |
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Term
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Definition
(referred to
as Bias) – error that results in a
distortion of conclusions. |
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Term
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Definition
error
incurred when some subjects
refuse to respond to a survey
Example: Estimating percentage of car crashes that
involve alcohol. Drunk drivers usually refuse the
BAC test. |
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Term
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Definition
occurs when certain items are
excluded from the sampling
frame. |
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Term
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Definition
when data collected do not reflect
the true measures.
Example: CDC Hepatitis Survey, Teacher Evaluation
requiring you to fill in your
name, order of survey
questions, etc. |
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Term
To make conclusions
about the population based upon
a single sample. |
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Definition
Major Goal of inferential
statistics: |
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Term
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Definition
The
sample proportion is used to
estimate the actual proportion of
the votes that each candidate will
get from the population of voters
(i.e: those people who actually get
out and vote). |
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Term
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Definition
( ) as a random
variable itself.Definition: The set of all possible
sample statistics ( ’s) could be
represented by a histogram, or
distribution. This distribution is
referred to as a sampling distribution. |
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Term
mean and standard
deviation. |
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Definition
The sampling distribution has its
own |
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Term
| sampling distribution of the mean. |
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Definition
The distribution of all possible
sample means is called the |
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Term
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Definition
| If the population is not normal (or unknown), then the shape of the sampling distribution is normal for large samples (n≥30). |
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Term
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Definition
| consists of a single sample statistic used to estimate the true value of the population parameter (either µ or π). |
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Term
the
parameter of interest is the
population proportion of successes, π. |
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Definition
Remember
that for categorical variables |
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Term
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Definition
| items are selected based only on the fact that they are easy, inexpensive, or convenient to sample. |
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Term
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Definition
| you get the opinions of pre-selected experts in the subject matter |
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Term
| Simple random sample and Systematic sample |
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Definition
Simple to use
May not be a good representation of the population’s underlying characteristics |
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Term
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Definition
| Ensures representation of individuals across the entire population |
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Term
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Definition
More cost effective
Less efficient (need larger sample to acquire the same level of precision) |
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Term
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Definition
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Term
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Definition
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Term
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Definition
| good questions elicit good responses |
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Term
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Definition
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Term
| Coverage error or selection bias SE |
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Definition
| Exists if some groups are excluded from the frame and have no chance of being selected |
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Term
| Non response error or bias SE |
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Definition
| People who do not respond may be different from those who do respond |
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Term
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Definition
| Variation from sample to sample will always exist |
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Term
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Definition
| Due to weaknesses in question design, respondent error, and interviewer’s effects on the respondent (“Hawthorne effect”) |
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Term
Standard Error of the Mean
Note that the standard error of the mean decreases as the sample size increases |
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Definition
| A measure of the variability in the mean from sample to sample is given by the |
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Term
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Definition
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Term
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Definition
| As the sample size gets large enoughthe sampling distribution of the sample mean becomes almost normal regardless of shape of population |
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Term
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Definition
| single number, the sample statistic estimating the population parameter of interest |
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Term
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Definition
| provides additional information about the variability of the estimate |
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Term
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Definition
| provides more information about a population characteristic than does a point estimate |
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Term
| Point Estimate ± (Critical Value)(Standard Error) |
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Definition
| The general formula for all confidence intervals is: |
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Term
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Definition
| is the standard deviation of the point estimate |
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Term
| substitute the sample standard deviation, S |
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Definition
| If the population standard deviation σ is unknown, we can |
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Term
| adding an allowance for uncertainty to the sample proportion ( p ) |
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Definition
| An interval estimate for the population proportion ( π ) can be calculated by |
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Term
Sampling is less time consuming and less costly
Sampling provides an objective way to calculate the sample size in advance
Sampling provides results that are objective and defensible.
Because the sample size is based on demonstrable statistical principles, the audit is defensible before one’s superiors and in a court of law
Sampling provides an estimate of the sampling error
Allows auditors to generalize their findings to the population with a known sampling error.
Can provide more accurate conclusions about the population
Sampling is often more accurate for drawing conclusions about large populations.
Examining every item in a large population is subject to significant non-sampling error
Sampling allows auditors to combine, and then evaluate collectively, samples collected by different individuals. |
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Definition
| Six advantages of statistical sampling in auditing |
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