What are some best practices for using stratified sampling in Power BI?

Stratified sampling is a statistical sampling technique that can be used in Power BI to improve the accuracy and representatives of your data samples. Some reasons to use stratified sampling When working with large datasets: To account for variability within your data To reduce bias in your samples To improve the precision of your estimates To improve the efficiency of your sampling If you have limited resources or time to collect data, stratified sampling can help you to make the most efficient use of your resources by ensuring that you are collecting data from the most important subgroups or strata which can lead to more accurate and reliable insights and conclusions.

Stratified sampling divides the population into homogeneous subgroups and samples proportionally from each stratum, resulting in a more accurate and reliable sample for analysis. Representative sampling by dividing the population into homogeneous strata and sampling obtains sample that reflects the diversity of the population Improved precision: by ensuring that each stratum is adequately represented in the sample obtains reliable and accurate insights for individual segments Handling imbalanced data by sampling proportionally from each stratum, you can ensure that smaller or less frequent groups are still adequately represented in the sample. Efficient resource utilization by focusing efforts on the most critical or significant subgroups

I disagree with the notion that stratified sampling is always the best approach for every dataset in Power BI. While it's excellent for ensuring representation across different segments, it might not be necessary or even counterproductive for datasets where the inherent variability within segments is more critical than the variability between segments. In such cases, other sampling techniques, like cluster sampling or simple random sampling, might be more appropriate.

1.Determine the attributes/dimensions (age groups, regions, or product cat.) in data that will serve as the basis for creating strata 2.Create calculated columns (IF, SWITCH, or DAX) that assign each record to its corresponding stratum based on the criteria 3.Define sample sizes for each stratum. This can be based on requirements, stat. considerations, or objectives 4.Apply filtering/query steps ("Keep Rows" or "Filter Rows") to select the required number of records from each stratum based on the defined sample size 5.Merge/append the sampled data from each stratum into a dataset of a stratified sample ("Append Queries" or "Merge Queries") 6.Analyze the sample, perform data analysis, generate insights, and draw conclusions for each stratum

An example I’ve seen of a more advanced stratified sampling technique in Power BI involves using a combination of DAX functions and Power Query. In this approach, users first define their strata using DAX, then utilize Power Query to extract the desired sample size from each stratum. This method allows for more complex stratification criteria and can be especially useful when dealing with large datasets or when specific sampling conditions are required.

1. Look for variables that exhibit substantial variation across the population. 2. Consider variables that have an impact on the outcome as it can help uncover relationships, patterns, or differences in the outcome across strata. 3. Ensure that the chosen variables have sufficient data coverage and are of high quality. 4. Leverage SMEs to guide your selection of stratification variables to help ensure that the chosen variables align with the domain-specific considerations and nuances. 5. Consideration of potential interactions between variables --> it may be beneficial to stratify based on combinations of variables to provide a more comprehensive understanding of the relationships within the population.

It's absolutely depend on dataset, one example I’ve seen before about effective stratification was in a public health study where researchers were investigating the effects of a new vaccine. Instead of merely stratifying by age or gender, they also considered socio-economic status, urban vs. rural settings, and pre-existing health conditions. This multi-layered stratification provided a more nuanced understanding of the vaccine's efficacy across different population segments, leading to more actionable insights.

An example I’ve seen of the challenges of stratified sampling in Power BI is when users attempt to stratify based on a variable with too many unique values, one helpful thing I’ve found when dealing with stratified sampling in Power BI is to utilize the platform's visualization tools to inspect the distribution of each stratum before sampling. This allows for a more informed decision on whether the chosen stratification variables are indeed representative and if the sample sizes for each stratum are adequate.

After performing the statistical checks, analysts would also engage domain experts to review the results. These experts, with their deep knowledge of the subject matter, can provide insights into whether the stratified sample truly captures the nuances and intricacies of the population, adding an additional layer of validation to the process.

One of the first issues one might encounter is the data point limitation in standard Power BI visuals, even with stratified sampling, a user may hit the visual limitation on data points and one might need a custom visual to fully represent the data. See https://learn.microsoft.com/en-us/power-bi/visuals/power-bi-data-points