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Learn more. What exactly does 'representative sample' refer to? Ask Question. Asked 10 years, 1 month ago. Active 9 years ago. Viewed 43k times. I wonder what 'representative sample' might refer to.
Improve this question. Add a comment. Active Oldest Votes. Hope that clears things up a bit Improve this answer. John Doucette John Doucette 2, 1 1 gold badge 15 15 silver badges 24 24 bronze badges. These are the references to Kruskal, and Mosteller: Kruskal, W.
Representative sampling, I: Nonscientific literature. International Statistical Review, 47, 13— To change or withdraw your consent choices for Investopedia. At any time, you can update your settings through the "EU Privacy" link at the bottom of any page.
These choices will be signaled globally to our partners and will not affect browsing data. We and our partners process data to: Actively scan device characteristics for identification. I Accept Show Purposes. Your Money. Personal Finance. Your Practice. Popular Courses. Economy Economics. Representative Sample vs. Random Sample: An Overview When conducting statistical analyses, economists and researchers seek to reduce sampling bias to a near negligible level.
Key Takeaways When conducting statistical analyses, economists and researchers seek to reduce sampling bias to a near negligible level. A representative sample is a group or set chosen from a larger statistical population according to specified characteristics.
A random sample is a group or set chosen in a random manner from a larger population. Compare Accounts. The offers that appear in this table are from partnerships from which Investopedia receives compensation. This compensation may impact how and where listings appear. Develop and improve products.
List of Partners vendors. A representative sample is a subset of a population that seeks to accurately reflect the characteristics of the larger group. For example, a classroom of 30 students with 15 males and 15 females could generate a representative sample that might include six students: three males and three females. Samples are useful in statistical analysis when population sizes are large because they contain smaller, manageable versions of the larger group.
Sampling is used in statistical analysis methodologies to gain insights and observations about a population group. Statisticians can use a variety of sampling methods to build samples that seek to meet the goals of their research studies. Representative samples are one type of sampling method.
This method uses stratified random sampling to help identify its components. Other methods can include random sampling and systematic sampling. A representative sample seeks to choose components that match with key characteristics in the entire population being examined. Statisticians can choose the representative characteristics that they feel best meet their research goals. Typically, representative sample characteristics are focused on demographic categories.
Some examples of key characteristics can include sex, age, education level, socioeconomic status, and marital status. Generally, the larger the population being examined, the more characteristics that may arise for consideration. Choosing a sampling method can depend on a variety of factors. Representative samples are usually an ideal choice for sampling analysis because they are expected to yield insights and observations that closely align with the entire population group.
When a sample is not representative, it can be known as a random sample. While random sampling is a simplified sampling approach, it comes with a higher risk of sampling error which can potentially lead to incorrect results or strategies that can be costly. Random sampling can choose its components completely at random, such as choosing names randomly from a list.
Using the classroom example again, a random sample could include six male students. Your target sample size is how many people you need to reach to derive accurate insights from your study. In fact, trying to collect results from a larger sample size can add costs — without significantly improving your results.
This calculator will give you an idea of how many people you need to survey based on the size of the total population. Before you get too far into sample size, take a moment to consider representative samples, too.
They are two related, but different issues. The sheer size of a sample does not guarantee its ability to accurately represent a target population. When some parts of the target population are not included in the sampled population, we are faced with selection bias, which prevents us from claiming that the sample is representative of the target population. Convenience samples are just what they sound like: choosing respondents that we can conveniently reach without regard to their demographic data.
These samples include respondents who are easier to select or who are most likely to respond; they will not be representative of harder-to-select individuals.
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