Como testar e validar a precisão e a confiabilidade da amostragem no Power BI?

Simple Random Sampling: Each element in the population has an equal chance of being selected. For example, randomly selecting 10% of customers from a database. Stratified Sampling: The population is divided into homogeneous groups, and samples are randomly selected from each group. For example, sampling 10% of customers from each age group. Systematic Sampling: Selecting every nth element from the population. For example, selecting every 5th record from a sorted list of customers. Cluster Sampling: Dividing the population into clusters, randomly selecting some clusters, and then sampling all elements within the selected clusters. For example, randomly selecting a few stores from different cities and sampling all customers in those stores.

In Power BI, you can apply sampling using the Table.Sample function in the Query Editor. This is the most simplest function that allows you to specify the table to sample from and the number of rows or the fraction of rows to include in the sample: Table.Sample(Table, 100) where 100 is number of rows to be sample from the table.

Analyze the sampled data to assess its accuracy and reliability. Compare the results obtained from the sample with the expected outcomes based on the full dataset. This evaluation can involve statistical analysis, visual comparisons, or any other relevant validation techniques.

Compare the characteristics of the sample (e.g., mean, standard deviation) with the population to ensure representativeness. Use visualizations and summary statistics to identify any discrepancies.

If any discrepancies or issues are identified, review and refine the sampling parameters or methodology. Adjust the sampling criteria, increase the sample size, or modify the sampling method as needed to improve accuracy and reliability.

Sampling bias occurs when the method of selecting a sample causes it to systematically differ from the population in some way. This can lead to inaccurate conclusions about the population. Selection bias occurs when certain segments of the population are systematically excluded or included with different probabilities than others. Non-response bias occurs when the individuals who choose not to participate in the study differ systematically from those who do participate. Measurement error occurs when there are inaccuracies in the data collection process, leading to incorrect values being recorded. Voluntary response bias occurs when the individuals who choose to participate in a study are not representative of the entire population.

To test and validate the accuracy and reliability of sampling in Power BI, you can compare sample results to the full data set, vary the sample size, use statistical tests, and employ cross-validation. These approaches help assess accuracy, consistency, and potential biases in the sampling method. By comparing results side by side, observing consistency across different sample sizes, comparing statistical measures, and using cross-validation techniques, you can gain confidence in the accuracy and reliability of the sampling in Power BI.