Data Peeking

Data peeking is the practice of examining interim results from an ongoing experiment or study before its planned completion. This involves checking statistical outcomes or data trends before reaching the predetermined sample size or endpoint. The practice is also known as optional stopping when it involves making decisions about continuing or stopping based on these early observations.

Context and Usage

Data peeking commonly occurs in A/B testing, online controlled experiments, clinical trials, and academic research settings. Researchers, data scientists, product managers, and experimenters may engage in this practice when conducting statistical hypothesis testing or comparing experimental conditions. The term is frequently discussed in the context of statistical methodology, experimental design, and data analysis workflows where decisions about continuing studies based on preliminary results are possible.

Common Challenges

Data peeking can inflate type I error rates, leading to false positive conclusions where effects appear significant when none exist. This practice creates a discrepancy between nominal p-values and actual probabilities, compromising the validity of statistical inferences and confidence intervals. The bias introduced by repeated examinations can make results appear more definitive than warranted, potentially causing researchers to implement ineffective changes or draw incorrect conclusions from their data.

Related Topics: optional stopping, sequential testing, type I error, A/B testing, statistical significance, p-hacking, experimental design