Technically, a representative sample requires only whatever percentage of the statistical population is necessary to replicate as closely as possible the quality or characteristic being studied or analyzed. For example, in a population of 1,000 that is made up of 600 men and 400 women used in an analysis of buying trends by gender, a representative sample can consist of a mere five members, three men and two women, or 0.5 percent of the population. However, while this sample is nominally representative of the larger population, it is likely to result in a high degree of sampling error or bias when making inferences regarding the larger population because it is so small.

Sampling bias is an unavoidable consequence of employing samples to analyze a larger group. Obtaining data from them is a process that is limited and incomplete by its very nature. But because it is so often necessary given the limited availability of resources, economic analysts employ methods that can reduce sampling bias to statistically negligible levels. While representative sampling is one of the most effective methods used to reduce bias, it is often not enough to do so sufficiently its own.

One strategy used in combination with representative sampling is making sure that the sample is big enough to optimally reduce error. And while, in general, the larger the subgroup, the more likely that error is reduced, at a certain point, the reduction becomes so minimal that it does not justify the additional expense necessary to make the sample larger.

Just as the use of a technically representative but tiny sample is not enough to reduce sampling bias on its own, simply choosing a large group without taking representation into account may lead to even more flawed results than using the small representative sample. Returning to the example above, a group of 600 males is statistically useless on its own when analyzing gender differences in buying trends.

  1. What are the advantages of using a simple random sample to study a larger population?

    Learn how simple random sampling works and what advantages it offers over other sampling methods when selecting a research ... Read Answer >>
  2. What is the difference between a simple random sample and a stratified random sample?

    Learn about the differences between simple random sampling and stratified random sampling, and the advantages of each method. Read Answer >>
  3. How do I calculate the standard error using Matlab?

    Learn how to calculate the standard error for a sample statistical measure, such as the sample mean, using standard Matlab ... Read Answer >>
  4. What are the pros and cons of stratified random sampling?

    Stratified random sampling provides a more accurate sampling of a population, but can be disadvantageous when researchers ... Read Answer >>
  5. What is a relative standard error?

    Find out how to distinguish between mean, standard deviation, standard error and relative standard error in statistical survey ... Read Answer >>
  6. How does the U.S. Bureau of Labor Statistics calculate the unemployment rate published ...

    Understand the process used by the U.S. Bureau of Labor Statistics to determine the official unemployment rate for the United ... Read Answer >>
Related Articles
  1. Personal Finance

    Birch Box Review: Is It Worth It?

    Learn more about the convenience of the subscription beauty box industry, and discover why the Birchbox company in particular has become so popular.
  2. Investing

    How Vanguard Index Funds Work

    Learn how Vanguard index funds work. See how the index sampling technique allows Vanguard to charge low expense ratios that can save investors money.
  3. Investing

    Behavioral Bias: Cognitive Versus Emotional Bias in Investing

    We all have biases. The key to better investing is to identify those biases and create rules to minimize their effect on investing decisions.
  4. Investing

    Leading Economic Indicators Predict Market Trends

    Leading indicators help investors to predict and react to where the market is headed.
  5. Tech

    New Advances In AI From Google Acquisition DeepMind (GOOG, AAPL)

    DeepMind's text-to-speech system can reproduce human speech patterns and produce music.
  6. Personal Finance

    How Women Uniquely View Finance and Investing

    Understanding gender differences on financial-related issues and how they are changing over time is fundamental to understanding the investing world.
  7. Financial Advisor

    Behavioral Finance: How Bias Can Hurt Investing

    Here are three cognitive biases from behavioral finance that investors would do well to be aware of to avoid making poor investment decisions.
  8. Personal Finance

    Women And Finances: Is There A Gender Bias?

    Uncover some very complex reasons for female gender biases in the finance world.
  9. Investing

    Mutual Fund Returns: Not Always What They Appear

    Survivorship bias erases substandard performers, distorting overall mutual fund returns.
  1. Representative Sample

    A representative sample is a subset of a statistical population ...
  2. Sampling Distribution

    A sampling distribution is a probability distribution of a statistic ...
  3. Sampling Error

    A sampling error is a statistical error that occurs when an analyst ...
  4. Sample Size Neglect

    Sample size neglect occurs when an individual infers too much ...
  5. Frequency Of Exclusion

    Frequency of exclusion refers to the rate of occurrence of a ...
  6. Confidence Interval

    A confidence interval measures the probability that a population ...
Trading Center