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In statistics and machine learning, temporal bias is an error or distortion in reasoning, data analysis, or predictive modeling that occurs when the timing of events or data collection is handled improperly or ignored.[1][2][3] The concept is recognized across different disciplines, including epidemiology, machine learning, and cognitive psychology.[1][3][4][5][6] Its exact definition and implications may vary by field.[1][3][5]
Causal inference
editIn epidemiology, temporal bias occurs when a study design cannot establish the correct sequence of events between an exposure and an outcome.[2][3] It is sometimes referred to as temporal ambiguity or reverse causation.[2][3] Establishing temporality is a major criterion for causal inference.[2][3]
This bias is common in observational research, such as cross-sectional studies, where variables are measured simultaneously at a single point in time.[2][3][7] For example, if a cross-sectional study finds an association between education level and health outcomes, researchers may be unable to determine whether lower education led to poor health or if poor health led to lower education.[1][2][7] Temporal bias can also occur in case-control studies when the time period from which the data is drawn does not accurately represent the timeline clinicians experience during the actual diagnostic process.[1] This misalignment can also inflate the predictive power of variables that appear close to the time of an outcome, which will undermine the reliability of the study's predictions.[1]
A form of temporal bias is spurious correlation resulting from time, where non-causal associations in data may appear as the result of shared connections to time.[3][8]
Artificial intelligence
editIn machine learning and artificial intelligence, temporal bias can manifest as experimental evaluation errors or as historical bias within training data.[4][9]
When evaluating models, temporal bias occurs if the training and testing datasets are randomly split without respecting the chronological order of the data.[4] This incorrect time split allows the model to learn from future information that would not logically be available in a real-world deployment, leading to artificially inflated performance metrics. This may also indirectly lead to an ambiguous knowledge cutoff.[4][10]
Additionally, temporal bias occurs when training data reflects historical inequalities or biases that existed during data collection, rather than the current context.[9] For example, an artificial intelligence hiring system trained on historical employment data may perpetuate past inequalities if certain demographics were historically underrepresented in high-level roles.[9]
Human psychology
editIn cognitive psychology, temporal biases may describe how human perception and decision-making are distorted by time.[5][11] A primary form is a bias towards the near future, where individuals overvalue immediate events and undervalue long term or past consequences, which influences their delay of gratification.[5]
Temporal bias also manifests as temporal distortion, where individuals inaccurately perceive the progression or duration of time based on emotional states.[11] For example, studies have shown that fear-inducing stimuli may cause temporal overestimation, meaning individuals will perceive threatening events as lasting longer than they actually do due to emotion-specific temporal biases.[11]
See also
editReferences
edit- 1 2 3 4 5 6 Yuan, William, et al. "Temporal bias in case-control design: preventing reliable predictions of the future." Nature Communications 12.1 (2021): 1107.
- 1 2 3 4 5 6 Savitz, David A., and Gregory A. Wellenius. "Can Cross-Sectional Studies Contribute to Causal Inference? It Depends." American Journal of Epidemiology 192.4 (2023): 514-516.
- 1 2 3 4 5 6 7 8 Taş, Pelin Gülüm; Maknoon, Yousef; Rezaei, Jafar (2026-01-20). "Time's Influence: A Systematic Review of Biases in Intertemporal Decision-Making". Annual Review of Psychology. 77 (77): 223–254. doi:10.1146/annurev-psych-091924-040158. ISSN 0066-4308. PMID 40829785.
- 1 2 3 4 Pendlebury, Feargus; Pierazzi, Fabio; Jordaney, Roberto; Kinder, Johannes; Cavallaro, Lorenzo (2019). "TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time". USENIX: 729–746. ISBN 978-1-939133-06-9.
- 1 2 3 4 McGuire, Joseph T., and Joseph W. Kable. "Rational temporal predictions can underlie apparent failures to delay gratification." Psychological Review 120.2 (2013): 395.
- ↑ Koterov, A. N.; Ushenkova, L. N.; Biryukov, A. P. (2020-12-01). "Hill's Temporality Criterion: Reverse Causation and Its Radiation Aspect". Biology Bulletin. 47 (12): 1577–1609. Bibcode:2020BioBu..47.1577K. doi:10.1134/S1062359020120031. ISSN 1608-3059.
- 1 2 Fairchild, Amanda J., et al. "A sensitivity analysis for temporal bias in cross-sectional mediation." Psychological Methods 29.1 (2024): 101-118.
- ↑ Yule, G. Udny (1926). "Why do we Sometimes get Nonsense-Correlations between Time-Series?--A Study in Sampling and the Nature of Time-Series". Journal of the Royal Statistical Society. 89 (1): 1–63. doi:10.2307/2341482. ISSN 0952-8385. JSTOR 2341482.
- 1 2 3 IBM. "What is Data Bias?" Retrieved 25 May 2026.
- ↑ Colton, Emma (2023-06-28). "Can ChatGPT discuss current events? Chatbot has clear knowledge cutoff date". Fox News. Retrieved 2026-05-25.
- 1 2 3 Tipples, Jason. "When time stands still: fear-specific modulation of temporal bias due to threat." Emotion 11.1 (2011): 74-80.