Quelle: Self-Tracking-Daten
Werkzeug: Python
Bei Tagen mit mehreren Momenten wird nur einer aufgezeichnet. Daher ist die tatsächliche Anzahl höher.
Von IAmMadeOfMe
Quelle: Self-Tracking-Daten
Werkzeug: Python
Bei Tagen mit mehreren Momenten wird nur einer aufgezeichnet. Daher ist die tatsächliche Anzahl höher.
Von IAmMadeOfMe
5 Kommentare
Your numbers only confirm to me what I knew to be true.
Context? All i can see is s bunch of gibberish. Who is this baader guy and what does he count
The Baader-Meinhof phenomenon, also called the frequency illusion, is when you notice something for the first time and then suddenly start seeing it everywhere.
It’s something to do with selective memory, making the repeated appearances noticeable.
The criteria I used was if I learnt a word or heard a new name, or things like that, if I see it again within a span of next few days, maximum a couple of weeks, then that becomes a data point. If the same thing was noticed again multiple times, it’s not counted (so the count is still one for that item). If a day had multiple moments (of different items), it’s counted as one moment per day.
You know, I just read about the Baader-Meinhof phenomenon the other day. Weird coincidence.
tbh the most interesting thing here is that you attempted to track this at all. This vizualization seems pretty arbitrary to me, no offense. I guess the hypothesis you’re exploring is that the likelihood of a Baader-Meinhof event is somehow correlated to month, which is why you’ve chosen to plot month, but I don’t really seen any particularly meaningful trend here. jul-dec is sort of bouncy, maybe that’s interesting? why do you think its bouncy? The data here is so small, its hard to say even that bounciness vs the relative stability jan-jun is real vs sample bias.