I’m now more confident in my understanding of the 95% confidence interval, but less certain about confidence intervals in general, knowing that we can’t be sure if our current interval includes the true population parameter. On a brighter note, if we have the correct confidence interval, it could still encompass the true parameter even when it’s not statistically significant. I find that quite refreshing
Simulating a binary dataset, coupled with an understanding of the logit link and the linear formula, is truly fascinating! However, we must exercise caution regarding our adjustments, as they can potentially divert us from the true findings. I advocate for transparency in Directed Acyclic Graphs (DAGs) and emphasize the sequence: causal model -> estimator -> estimand.
Saving can be enjoyable! If you’re planning to cut down on takeout orders, why not use past data to simulate your savings? Let it inspire and motivate your future dining-in decisions! 👍
How to solve this… 2 ? 1 ? 6 ? 6 ? 200 ? 50 = 416.56
Brief Introduction: The 100 prisoners problem is a probability theory and combinatorics problem. In this challenge, 100 numbered prisoners must find their own numbers in one of 100 drawers in order to survive. Rules: We have 100 prisoners labeled: 1, 2 … 100 on their clothes we have a room filled with 100 boxes labeled 1, 2, … 100 on the outside of the boxes inside each box, there is a number from 1, 2 … 100 only 1 prisoner may enter the room each time Each prisoner may open only up to 50 attempts/boxes and cannot communicate with other prisoners if the prisoner found his/her/their number, he/she/they will exit the room and no be able to talk to other prisoners.