Big data and large sample size: a cautionary note on the potential for bias.

Area of Science:

• Biostatistics
• Epidemiology
• Health Services Research

Background:

• Growing interest in large-scale studies and
• big data
• integration of diverse data sources.
• Commentaries suggest large studies offer greater reliability than smaller ones.
• Potential for large sample sizes to enhance research insights.

Purpose of the Study:

• To identify and discuss potential biases inherent in
• big data
• research.
• To caution against inferential errors arising from large sample sizes.
• To emphasize the need for careful consideration of study design and data quality in large studies.

Main Methods:

• Review and discussion of common biases in
• big data
• analysis.
• Examination of potential errors including sampling, measurement, multiple comparisons, aggregation, and information exclusion.
• Illustrative examples from epidemiology, health services research, determinants of health studies, and clinical trials.

Main Results:

• Large sample sizes in
• big data
• research can amplify existing biases.
• Specific biases discussed include sampling error, measurement error, multiple comparisons, aggregation error, and systematic exclusion of information.
• Large studies may magnify inferential errors if not carefully managed.

Conclusions:

• Caution is necessary to prevent large sample sizes from causing significant inferential errors.
• The advantages of large studies can be undermined by magnified biases.
• Rigorous methodological approaches are crucial for reliable
• big data
• research.