Sample size

Definition

Sample size refers to the number of individuals or observations used in a statistical sample. It's crucial because it impacts the reliability and accuracy of the results derived from the research process, influencing measures like central tendency and dispersion, as well as the overall validity of findings.

5 Must Know Facts For Your Next Test

1. A larger sample size generally leads to more accurate results because it reduces sampling error and provides a better representation of the population.
2. Determining sample size involves balancing cost and time with the need for precision in research results.
3. Statistical formulas, such as those involving confidence intervals and margin of error, are often used to calculate the optimal sample size.
4. Non-response can affect sample size calculations; adjustments may need to be made to ensure adequate representation.
5. Data quality can be compromised if the sample size is too small, leading to unreliable conclusions about trends or behaviors in the target population.

Review Questions

• How does sample size influence measures of central tendency and dispersion in research?
  • Sample size plays a critical role in determining the accuracy of measures of central tendency, such as mean and median, and measures of dispersion like range and standard deviation. A larger sample tends to provide a more accurate reflection of these statistics, reducing the likelihood of outliers skewing results. Conversely, a small sample size may lead to misleading averages and variability estimates that don't accurately represent the population.
• Discuss the steps involved in determining an appropriate sample size during the research planning phase.
  • When determining an appropriate sample size, researchers begin by defining their target population and identifying key characteristics that must be represented. Next, they assess desired confidence levels and margins of error to guide calculations. Statistical formulas can then be applied to estimate the minimum sample size needed for reliable results. Lastly, factors like budget constraints and available resources are considered to finalize the sample size.
• Evaluate how non-response rates impact sample size adequacy and overall data validity in marketing research.
  • Non-response rates can significantly undermine the adequacy of a sample size by introducing bias if certain groups are less likely to respond. This can distort findings and reduce overall data validity. To combat this, researchers may increase the initial sample size to account for expected non-responses or utilize follow-up techniques to enhance participation rates. Ensuring a representative sample is vital for making sound marketing decisions based on valid data.

Related terms

Population:

The entire group of individuals or instances about whom we hope to learn, from which a sample is drawn.

Sampling Error: The error that occurs when a sample does not perfectly represent the population, leading to inaccurate conclusions.

Confidence Level:

A statistical measure that expresses how confident we are that the sample accurately reflects the population, often expressed as a percentage.