**Mismatching epistemological hierarchies** explain perhaps a quarter of #debates that end up unproductive or even bitter for me.

I wouldn't dare define my _Pyramid of Sources of #Truth_ without giving it some careful thought first, but… a first approximation:

💡 **Quantitative data or stats that are either common knowledge or directly accessible to most people** (eg, no. of seats in the Spanish Parliament, minimum height of all outside doors in Granada Cathedral).
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💡 **Quantitative data or stats issued by sources commonly understood to be authoritative and unbiased** (eg, average size of all dentist's offices in Norway according to the Ministry of Health).
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💡 **Old, peer-reviewed, published meta-analyses that are accessible** (eg via #SciHub) **to most people**.
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💡 **Old, peer-reviewed, published meta-analyses**.
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💡 **Old, peer-reviewed, published papers**.
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💡 **Peer-reviewed, published papers**.
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💡 **Peer-reviewed papers**.
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💡 **Reports, surveys, research, whitepapers, polls** — with a wild degree of confidence, depending on the particulars (see _parameters_ below).
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💡 **Published books, theatrical documentaries**.
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💡 **Self-published books, self-produced documentaries**.
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💡 **Long blog posts with links to secondary sources**.
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💡 **Newspaper articles** fall somewhere around here.
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💡 **Blog posts**.
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💡 **Online videos, tweets, screenshots, photos, voice messages, viral clips, copied-and-pasted quotes, hearsay, anecdote, feeling, hunch, rumour, revelation**.

In all cases, the bigger these **parameters**, the higher a particular #sourceOfTruth moves up my hierarchy:

🔺 Sample size.
🔺 No. ot times result has been reproduced.
🔺 Boringness of result (ie, how common-sensical and unsurprising it seems to the average person).
🔺 Parsimoniousness (ie, simplicity) of interpretations given for result.
🔺 Awkwardness of result for parties involved (ie, how inconvenient it is for the interests of authors themselves).