How do you determine the optimal sample size for EDA?

2 Why is sample size important?

Sample size is important because it influences the margin of error and the confidence level of your EDA findings. Margin of error is the amount of uncertainty or error that you can tolerate in your estimates or conclusions. Confidence level is the degree of certainty or confidence that you have in your estimates or conclusions. Generally, a larger sample size reduces the margin of error and increases the confidence level, meaning that your EDA results are more accurate and reliable. However, a larger sample size also requires more data collection and processing, which can be costly and time-consuming.

4 How to optimize sample size?

Optimizing sample size for an EDA project is about finding the optimal balance between accuracy and feasibility. This will depend on the specific context and objectives of your analysis. Generally, it’s helpful to define a research question and hypothesis to guide the process and determine the scope and level of detail needed. Stratified or clustered sampling is another way to ensure a representative and diverse sample without compromising accuracy. Additionally, data cleaning and validation techniques can help reduce noise and bias in the data, as well as improve the efficiency and effectiveness of the EDA process. These techniques include data profiling, transformation, imputation, normalization, and verification.

5 How to evaluate sample size?

Evaluating sample size for an EDA project means assessing its adequacy and suitability. Descriptive statistics, such as the mean, median, mode, standard deviation, range, frequency, or distribution can help determine if the sample size is large enough to capture the variability and diversity of the data. Inferential statistics like the t-test, ANOVA, correlation, or regression can help decide if the sample size is appropriate for the type and complexity of the analysis. Visualizations like charts, graphs, plots, or maps can help assess if the sample size is suitable for the format and style of a visualization. All of these measures can ensure that the sample size is large enough to show patterns, trends, outliers, and relationships in your data.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?