A statistical error is the difference between a sample value and the true population value. There are two main types of error - sampling error and non-sampling error. Sampling error occurs when the sample is not fully representative of the population, while non-sampling error can arise from factors like non-response, measurement issues, interviewer errors, adjustments to the data, or processing mistakes. Common ways to measure and reduce sampling error include calculating the standard error, sample size, and sample design.

1. What is error
Definition: A statistical
error is the (unknown)
difference between the
retained value and the true
value.

2. Sampling vs non-sampling error

3. Sampling error
• statistical error that occurs when an analyst does
not select a sample that represents the entire
population of data.
 As a result, the results found in the sample do not
represent the results that would be obtained from the
entire population.
• The difference between the values derived from
the sample of a population and the true values of
the population parameters is considered a
sampling error.

4. Factors Affecting Sampling Error
Sampling error is affected by a number of
factors including
sample size, sample design, the sampling
fraction and the variability within the
population.
In general, larger sample sizes decrease the
sampling error, however this decrease is not
directly proportional.

5. Categories of Sampling Errors
• Population Specification Error – Happens when the
analysts do not understand who to survey. For
example, for a survey of breakfast cereals, the
population can be the mother, children, or the entire
family.
• Selection Error – Occurs when the respondents’
survey participation is self-selected, implying only
those who are interested respond. Selection errors
can be reduced by encouraging participation.
• Sample Frame Error – Occurs when a sample is
selected from the wrong population data.

6. Categories of sampling error…
• Non-Response Error – Occurs when a useful response
is not obtained from the surveys. It may happen due to
the inability to contact potential respondents or their
refusal to respond.
. Sampling Errors- Sampling errors occur when there is a
lack of representativeness of the target population in
the sample group.
This is generally the result of poor sample designing

7. Measure of sampling error
• Standard Error
The most commonly used measure of sampling
error is called the standard error (SE).
• The standard error is a measure of the spread of
estimates around the "true value".
• A small standard error indicates that the variation
in values from repeated samples is small and,
hence there is more likelihood that the sample
estimate will be close to the result of an equal
complete coverage.

8. Measures of….
• Variance
The variance is another measure of sampling error,
which is simply the square of the standard error
• Relative Standard Error
Another way of measuring sampling error is the
relative standard error (RSE) where the standard error
is expressed as a percentage of the estimate.
• The RSE avoids the need to refer to the estimate
• useful when comparing variability of population
estimates with different means.
• Confidence interval:

9. How to Estimate the Sampling Error?
. .
The margin of error
that is seen in survey
results is an estimate
of sampling error

11. What are the steps to reduce
sampling errors?
• Increase sample size
• Divide the population into groups: Test
groups according to their size in the
population instead of a random sample.
• Know your population

12. Non-sampling error
• The error that arises in a data collection process
as a result of factors other than taking a sample.
• It is different from sampling error, which is any
difference between the sample values and the
universal values that may result from a limited
sampling size.
• Non-sampling errors have the potential to
cause bias in polls, surveys or samples.

13. Types of Non-Sampling Errors
1. Non-response error
 it exists when people are given the option to
participate but choose not to; therefore, their
survey results are not incorporated into the data.
2. Measurement error
• A measurement error refers to all errors relating
to the measurement of each sampling unit.
• The error often arises when there are confusing
questions, low-quality data due to sampling
fatigue (i.e., someone is tired of taking a survey),
and low-quality measurement tools.

14. 3. Interviewer error
• Interviewer error occurs when the interviewer (or
administrator) makes an error when recording a
response.
• 4. Adjustment error
• An adjustment error describes a situation where
the analysis of the data adjusts it so that it is not
entirely accurate. Forms of adjustment error
include errors with weighting the data, data
cleaning, and imputation

15. • 5. Processing error
• A processing error arises when there is a
problem with processing the data that causes
an error of some kind. An example will be if
the data were entered incorrectly or if the
data file is corrupt.