Скачать или смотреть How Do Data Gaps Impact Statistical Model Assumptions?

Ever wondered how missing pieces in your dataset can quietly sabotage your statistical models? This video unpacks the critical ways data gaps undermine the foundational assumptions of statistical analysis, leading to skewed results and unreliable conclusions.

In this insightful discussion, we explore:
► How data gaps, even seemingly small ones, can introduce significant bias into your statistical findings.
► The direct link between missing data and the violation of crucial model assumptions like normality and homoscedasticity.
► Why ignoring data gaps can lead to reduced statistical power, making it harder to detect real effects.
► The importance of understanding different types of missing data mechanisms (MCAR, MAR, MNAR) and their unique challenges.
► Practical implications for data scientists and researchers in ensuring robust and valid model outputs.


#DataGaps, #StatisticalModels, #DataScience, #Analytics, #MissingData