Increasing sample size benefits increase the confidence and reliability of the confidence interval, and thus the precision with which the population parameter can be estimated.
Factors that make larger sample size undesirable
- Additional expenses
- Population parameters have a tendency to change over time
Potential mistakes in sampling:
Data-snooping bias
- Conclusions of one analyst are guided by conclusions of others.
Data-mining bias
- Analyst keeps searching for patterns & trading rules until he find one that matches the data.
Sample selection bias
- Refer to the tendency to exclude a certain part of a population simply because the data is not
available.
Survivorship bias
- Only funds/stocks that have survived to date are included. Big problem with stock indices.
Look-ahead bias
- Based on information that do not actually exist at the time of analysis, just based on analyst’s
assumptions.
Time-period bias
Conclusions may apply only to a specific time period and are not repeatable over longer
time periods. Either too short or too long.
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