The hidden effect of time

Area of Science:

• Statistics
• Data Analysis
• Research Methodology

Background:

• Standard practice assumes dataset homogeneity regarding measurement collection order.
• This assumption can be violated, leading to potential biases.
• Understanding temporal dependencies is crucial for valid scientific conclusions.

Purpose of the Study:

• To highlight the implications of violating the homogeneity assumption in datasets.
• To introduce the concept of hidden time trends in data.
• To propose methods for detecting and addressing temporal effects in observational data.

Main Methods:

• Illustrating scenarios where the order of data collection is not random.
• Discussing the impact of time trends on study design and analysis.
• Suggesting graphical methods for temporal trend detection, such as plotting data against time or using cumulative sum (cusum) plots.

Main Results:

• Demonstrated that datasets may not be homogeneous over time.
• Identified hidden time trends as a significant factor affecting data interpretation.
• Showcased the utility of time-based plots for revealing underlying trends.

Conclusions:

• The assumption of dataset homogeneity with respect to collection order should be critically evaluated.
• Incorporating temporal analysis, like time-series plotting or cusum analysis, is essential when observation order is known.
• Addressing hidden time trends improves the rigor of study design, analysis, and interpretation.