Can the Data Be Right While the Diagnosis Is Wrong? A Case Study
Aydın Tiryaki (2026)
It all started on January 22, 2026, when the Central Bank of the Republic of Türkiye (CBRT) announced its first interest rate decision of the year. As an engineer, irregularities in systems always catch my attention. When I looked at the Central Bank’s meeting schedule, I noticed a structure that deviated from the “same day every month” regularity we are accustomed to; it was irregular, expanding and contracting almost like an accordion.
Combined with my concerns regarding the political economy in Türkiye, my hypothesis was quite clear: “Are these meeting dates being changed arbitrarily based on the political conjecture or immediate pressures?”
To pursue this question, I embarked on a deep data analysis with my AI assistant. Our goal was to compare Türkiye’s schedule with global standards, specifically the US Federal Reserve (FED) and the European Central Bank (ECB). However, this journey would teach me not only about economics but also about the “Blind Spots” of working with Artificial Intelligence.
1. The Language of Data: Statistical Evidence and the “Accordion” Schedule
When we laid the 2025 calendars on the table with the AI, we encountered a striking picture that seemed to confirm my suspicions:
- CBRT (Türkiye): There was an incredible variance in the meeting intervals. For instance, there was a massive 63-day (9-week) gap between the April and June meetings, followed immediately by a mere 35-day (5-week) interval for the July meeting.
- FED (USA) and ECB (Europe): They operated like a Swiss watch. The FED had set its schedule with almost no deviation, meeting strictly every 42 or 49 days.
To prove this observation mathematically, I asked the AI to calculate the Standard Deviation of the meeting intervals for these three banks. The results supported my hypothesis:
- FED Standard Deviation: 3.74 (Extremely regular)
- CBRT Standard Deviation: 8.91 (Highly irregular and volatile)
Numbers do not lie. This high deviation appeared to be mathematical proof of “momentary interventions” or “arbitrary postponements” when viewed from the outside. At that exact moment, I was about to write an article themed “Political Uncertainty in the Central Bank Schedule.”
2. The Critical Turning Point: “What About Next Year?”
The analysis was finished, and the verdict was in. However, my engineering intuition told me to subject the system to one final “Stress Test.” So, I asked the saving question:
“So, is the meeting schedule for 2026 known right now?”
The AI’s answer turned my entire analysis upside down: “Yes, the 2026 schedule is known. In fact, the irregular 2025 schedule we analyzed was announced in the Official Gazette in December of the previous year, and it was adhered to without the slightest deviation throughout the year.”
3. What Was the Reality?
It turned out that what I perceived as “arbitrary irregularity” was actually the product of a very strict bureaucratic planning:
- The 8+4 Rule: The Central Bank does not hold interest rate meetings in the months it releases the Inflation Report (February, May, August, November).
- Mandatory Gaps: That massive 63-day gap I mistook for “political postponement” was actually a mandatory schedule shift caused by the clash between the Inflation Report presentation in May and the Eid al-Adha holiday in early June.
In short, the schedule was designed to be “irregular,” but the implementation was “disciplined.” A pre-announced plan had been strictly followed.
4. Not a Hallucination, But a “Blind Spot”
In this incident, the AI did not lie to me. It did not commit the error known in literature as “Hallucination” (fabricating non-existent data). It provided the dates and day intervals correctly.
However, it made a much more insidious error: A Blind Spot.
When I told it to “Compare the dates,” it focused solely on the mathematical data. It did not include the legal ground where that data originated, the reality of the “Official Gazette,” and the legislative context (the larger matrix) in the analysis.
- It gave the data but missed the context.
- Instead of refuting my wrong hypothesis, it led me to a wrong conclusion (Confirmation Bias) with the incomplete context it provided.
Conclusion: Matrix Thinking
This experience holds a critical lesson for AI literacy. Large Language Models (LLMs), no matter how advanced, cannot view events like a “Systems Engineer.” They struggle to connect seemingly unrelated dots (Calendar Dates – Official Gazette – Religious Holidays) to construct a large matrix.
If I hadn’t asked that final question, I would have had an article today that was “written with correct data but completely wrong in its conclusion.”
Working with AI is not just about asking questions; it is about constantly questioning the ground upon which the question sits. AI is a fantastic co-pilot, but the Human Mind must remain the one to determine the route, check for “Blind Spots,” and draw the logical framework.
A Note on Methods and Tools: All observations, ideas, and solution proposals in this study are the author’s own. AI was utilized as an information source for researching and compiling relevant topics strictly based on the author’s inquiries, requests, and directions; additionally, it provided writing assistance during the drafting process. (The research-based compilation and English writing process of this text were supported by AI as a specialized assistant.)
Aydın Tiryaki 