A register of questions we are aware we cannot currently answer. This list documents the boundaries of our knowledge and prevents false completeness.
Most research platforms present findings as complete. This creates an illusion of total knowledge. By documenting what we cannot answer, we signal epistemic maturity and train readers to respect uncertainty as a form of rigor.
What we cannot answer: How do behavioral patterns differ between urban and rural Gen Z populations in India?
Why we cannot answer it: Digital access constraints prevent representative rural sampling. Our survey platform requires internet connectivity and English literacy.
What this affects: All current insights apply only to urban, digitally-connected youth. Generalizations to India's full 18–25 population are invalid.
Possible future resolution:Offline survey partnerships with rural educational institutions. Requires funding and multilingual survey design.
What we cannot answer: How do emotional self-reports differ when expressed in regional languages versus English?
Why we cannot answer it: Surveys conducted exclusively in English. Emotional vocabulary varies significantly across languages and may not translate directly.
What this affects: Mood scores, anxiety metrics, and identity-related responses may reflect English-language framing rather than authentic emotional states.
Possible future resolution:Multilingual survey deployment with culturally-adapted emotional scales. Requires linguistic validation studies.
What we cannot answer: How do offline social networks shape behavior differently than digital networks?
Why we cannot answer it: Our data captures self-reported behavior but cannot observe actual peer interactions, family dynamics, or community influence in physical spaces.
What this affects: Behavioral drivers may be attributed to digital trends when offline social pressure is the actual cause.
Possible future resolution:Ethnographic studies or longitudinal observation research. Cannot be resolved through surveys alone.
What we cannot answer: How do spending patterns differ across specific income brackets within urban Gen Z?
Why we cannot answer it: Income data is self-reported and often inaccurate. Respondents may not know household income or may misreport due to social desirability bias.
What this affects: Economic pressure insights may conflate different income realities. A ₹30,000/month household and ₹2,00,000/month household are both "urban Gen Z" in our data.
Possible future resolution:Partner with financial institutions for anonymized transaction data. Raises significant privacy concerns.
What we cannot answer: Why do observed patterns occur? What are the actual causal mechanisms?
Why we cannot answer it:Observational data shows correlation, not causation. We document patterns but cannot isolate variables or run controlled experiments.
What this affects: All interpretations are hypotheses, not proven explanations. Drivers listed in insights are educated guesses.
Possible future resolution:Experimental research designs or longitudinal tracking. Requires significantly more resources and ethical oversight.
What we cannot answer: How long does it take for cultural shifts to appear in survey responses?
Why we cannot answer it: We capture present-moment self-reports. Behavioral changes may occur weeks or months before respondents consciously recognize and report them.
What this affects: Trends may appear later in our data than they actually began. "Emerging" patterns may already be stabilized in reality.
Possible future resolution:Increase survey frequency or add retrospective questions. Both have limitations.
For researchers: Cite these limitations when using Fomofiles data. Do not extrapolate beyond documented boundaries.
For educators: Use this register to teach students about epistemic humility and research constraints.
For journalists: Reference these gaps when contextualizing insights. Avoid presenting findings as complete truth.
For contributors: Understand what your data can and cannot reveal. Your participation helps, but does not eliminate these gaps.
This register is updated quarterly. New unknowns are added as we discover them. Resolved unknowns are moved to a separate archive with documentation of how they were addressed. Last updated: January 2025.