What Are the Consequences of High Data Variability?
High data variability can have several negative impacts:
Reproducibility: High variability can lead to challenges in reproducing results, which is crucial for validating scientific findings. Interpretation: It can complicate the interpretation of results and make it difficult to draw meaningful conclusions. Optimization: Variability can hinder the optimization of catalytic processes, as inconsistent data can obscure the effects of different parameters. Resource Wastage: Increased variability can result in wasted resources, as more experiments may be needed to achieve reliable data.
