The document explains sampling error as the difference between a sample's characteristics and the population it represents, crucial for the reliability of educational research findings. It identifies common sources of sampling error like selection bias and non-response bias, and discusses the importance of minimizing these errors for evidence-based decision making. Strategies to reduce sampling error include increasing sample size, utilizing random sampling, and establishing clear selection criteria.

1. Sampling Error
Dr. Sarita Anand
Assistant Professor
Department of Education
Vinaya Bhavana
Visva-Bharati, Santiniketan
W.B. India

2. Definition
• Sampling error is a critical aspect of educational
research, influencing the reliability of findings.
• Sampling error refers to the discrepancy between the
characteristics of a sample and the population it
represents.
• It arises due to random variability in the sampling
process and can impact the accuracy and reliability
of research findings.

3. Importance in Educational Research
Validity of Findings
Understanding sampling
error ensures the accuracy
of research findings.
Quality of Analysis
It aids in ensuring the
reliability and rigorousness
of data analysis.
Evidence-Based Decisions
Minimizing sampling error leads to informed and
evidence-based decision-making.

4. Common sources of sampling error
Selection Bias
Occurs when the sample is not
representative of the population due to
systematic differences in
characteristics.
Non-Response Bias
Arises when non-respondents differ
from respondents in ways that affect
the research outcomes.
Sampling Frame Errors
Result from incomplete or inaccurate
lists used to identify the population of
interest.
Measurement Errors
Involve inaccuracies or imprecisions in
data collection methods or instruments.

5. Sampling Error & its Calculation
Statistic: Represents a summary description of the sample i.e. Estimated Value (an estimate of the parameter
derived from sample)
e.g. Average age of the sample of 10 M.Ed. Students= ⅀10 M.Ed. Student’s Age/10=→Statistic Mean
Parameters: Characteristics of a population are called Parameters. “A parameter is the summary description
of a variable for a population (Sanders & Pinhey ,1983).
e.g. Average age of M.Ed. Students= ⅀M.Ed. Student’s Age/N =→Parameter Mean
So, average age is the parameter of the population=all students, this is the True Value
So, discrepancy found between True value (parameter mean value) & Estimated Value (Statistic mean value) is
called sampling error.
A sampling Error is the difference between the Parameter & the Statistic.
Sampling Error=True Value ~ Estimate Value

6. Impact of Sampling Error on Research Findings
Underestimated
Variability
Sampling error can lead
to underestimating the
true variability in a
population, affecting
the generalizability of
research findings.
Misleading
Conclusions
Inaccurate
representation of the
population due to
sampling error can lead
to misleading
conclusions and
erroneous
interpretations.
Reduced
Accuracy
Sampling error can
result in reduced
accuracy of research
findings, impacting the
reliability of the study's
results.

7. Strategies to Minimize Sampling Error
• Increased sample size: Expanding the number of participants
to enhance representation.
• Random sampling: Utilizing random selection techniques for
participant inclusion.
• Clear criteria: Establishing precise inclusion/exclusion
criteria for participant selection.

8. Examples of Sampling error in Educational Research
Sampling
Improper
randomization
causing skewed
representation of
population.
Questionnaire
Bias
Leading
questions
influencing
participant
responses and
data reliability.
Data Collection
Method
Using outdated or
flawed methods
leading to
inaccurate results.
Sampling
Frame
Misaligned sampling
frame affecting
representativeness
of the sample.

9. Conclusion:
1
Understanding sampling error
Importance of considering sampling error in research
2
Common sources of error
Identifying and addressing potential sources of error
3
Minimizing error impact
Strategies to reduce the impact of
sampling error

10. References:
Cohen, L., Manion, L., & Morrison, K. (2007). Research methods in education (6th ed.).
Routledge.
Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed methods
approaches (4th ed.). Sage Publications.
Johnson, R. B., & Onwuegbuzie, A. J. (2004). Mixed methods research: A research
paradigm whose time has come. Educational Researcher, 33(7), 14-26.