When faced with a smaller-than-expected data sample, it’s all about making the most of what you have. I focus on maximising data quality by cleaning and analysing it thoroughly to ensure accuracy. Sometimes, combining your small sample with existing studies or secondary data can help strengthen the conclusions. It's about being smart with the analysis and acknowledging the limitations while still finding valuable insights.

When working with a small dataset, I prioritize using non-parametric tests or Bayesian analysis, as they are more resilient to limited sample sizes. I also focus on increasing measurement accuracy to reduce variability and gain clearer reflection.

To draw meaningful conclusions from a smaller-than-expected data sample, focus on several key strategies. Begin by utilizing descriptive statistics, such as means, medians, and ranges, to summarize the data and provide a clear overview of your findings. Incorporate qualitative analysis through methods like interviews or open-ended questions to gain deeper insights and enrich your understanding. Additionally, calculate confidence intervals to offer a range within which the true population parameter is likely to fall, helping to assess the reliability of your conclusions. Look for trends within the data that could inform your analysis, and ensure transparency by clearly stating the limitations of your small sample size in your findings.