Sample-size

Sample-size refers to the number of individual observations or data points included in a study or experiment. It is a crucial factor in statistical analysis as it directly impacts the reliability and generalizability of results. A larger sample-size typically leads to more precise estimates and increases the power of a study to detect meaningful effects, while a smaller one can increase the impact of random error, lowering the confidence we have in the observed values. The appropriate sample-size depends on factors such as the study's objectives, the expected effect size, the desired level of precision, and the variability of the population being studied. Researchers must carefully consider these factors when designing a study, ensuring the sample-size is adequate to address the research question effectively without expending unnecessary resources.

Sample-size meaning with examples

• In a clinical trial, a sample-size of 100 patients might be considered insufficient to reliably detect a small difference in treatment effectiveness, risking a false negative result. To address this, researchers may decide that they require more data. The researchers need a higher sample-size, perhaps 500 to reach enough statistical power, which allows them to detect a more effective drug with higher confidence.
• A market research firm used a smaller sample-size of 50 customers. The firm struggled to find a representative sampling across a diverse population when determining customer preferences for a new product. The skewed data resulting from this led to inaccurate conclusions about the product's market potential. It would be prudent to select a higher sample-size in the future.
• When studying the effectiveness of a new teaching method, educators chose a sample-size of only 15 students for each class group. The small sample-size meant the results could be significantly influenced by individual student differences, making it difficult to generalize findings to a larger student population, impacting the results of the test and, ultimately, the conclusions.
• A study analyzing the correlation between income and happiness, used a larger sample-size of 5000 people. With such a large sample-size, even small correlations could achieve statistical significance. This allowed the researchers to draw robust conclusions about the relationship between income and subjective well-being with much higher confidence in their findings.
• To investigate the impact of a new social media campaign on brand awareness, a company utilized a sample-size of 200 participants. The company's advertising executives quickly realised that such a small sample-size might only provide very limited insights and might not capture the full impact of the campaign on a much larger target audience.