Extend or constructively challenge this post: Assess the implications for professional practice when a researcher implies causation after using correlation (e.g., bivariate correlation) analyses. In a DBA doctoral study, a crucial distinction exists between correlation and causation. While correlation simply indicates that two variables move together, causation implies that one variable directly influences the other. Misinterpreting correlation as causation can have significant implications for professional practice. According to Mponda and Kaombe (2024), if a researcher uses bivariate correlation analysis to demonstrate an association between two variables and then claims a causal relationship without further evidence, their findings may be misleading and lead to incorrect decisions based on faulty assumptions. A study might show a correlation between employee satisfaction and company productivity. This does not necessarily mean that increased satisfaction causes increased productivity. There could be other underlying factors, like a positive work environment, that influence both. In professional practice, relying solely on correlational data to establish causation can lead to ineffective interventions or strategies. For instance, a company might invest in employee benefits based on a perceived correlation with satisfaction, only to find that the benefits have no actual impact on productivity. Researchers must be cautious when interpreting correlational findings and employ