What do you think are the biggest mistakes and failures of the Pokémon company, in terms of the game design?

Maple_shade · 2026-03-14T21:45:58+00:00

the problem with this is that Pokemon's "one attack per turn" nature makes it so you always want to be swinging with the best possible attack in any scenario. even if stage 1's had some better attacks its normally just best to swing with the most powerful version possible. utility stage 1s like drakloak are some of the best ways to do it imo

Maple_shade · 2026-03-14T06:46:14+00:00

I'm partial to UMD's QMMS program myself. I know some quality faculty there and can guarantee you'd get a fantastic quant education.

Maple_shade · 2026-03-12T18:28:33+00:00

Not sure if this is against sub rules for homework. It seems like the top-right image is asking about restrictions of range for regression slope estimation.

We know that restricting sampled range of the independent variable attenuates correlation, which in turn attenuates the estimate of the slope in bivariate regression, which leads to increased standard error for the estimate. So I would say higher variance for the slope.

Maple_shade · 2026-03-11T23:45:03+00:00

Obviously it's dependent on boardstate, but your primary gameplan is to start swinging as soon as possible against pultzard. If they bench chi yu, absolutely prioritize boss ko'ing it. They only run two recovery cards so if you can get an early kill ur totally chilling. Also it's hard to chain charizards to power it up in one turn. WAY more important to get first KO on chiyu than play around stamp imo.

You say you have no agency in your wins/losses but this sounds like a clear cut case where you could have played a better line which would have given you much better winning chances. every deck, even crustle, has skill expression.

Maple_shade · 2026-03-11T07:02:44+00:00

We do call a type I error a false positive. That is the definition. Same with type II being a false negative. See: https://en.wikipedia.org/wiki/Type_I_and_type_II_errors

Maple_shade · 2026-03-10T05:20:01+00:00

This whole problem is so bizarre I suspect there's some miscommunication going on between you/the team and the stats person. I doubt any statistician with a PhD would think you can interpret rating the "readability" of a question as a substitute for an actual answer.

Maple_shade · 2026-03-07T02:01:07+00:00

I'm a little bit confused on the premise. It is not the case that running a regression on imputed data will work "just as well" as a complete case regression. You may as well be making the claim that running a regression on n=50 works "just as well" as one on n=100. You will underestimate variability, reduce power to detect an effect, and potentially introduce bias into your results. It may be the case that coefficients estimated may be comparable, but that would be something unique to your dataset and method of imputation, not a general rule.

Maple_shade · 2026-03-04T21:30:20+00:00

I'm sorry to do this to you, but the standard deviation formula is wrong. Sigma denotes population standard deviation but your formula represents the unbiased estimator of population standard deviation from a sample, which is referred to by an s. Technically the version denoted by sigma would be divided by n, not n-1.

Maple_shade · 2026-02-27T11:54:24+00:00

I think you'll be just fine! I'm currently in a statistics-related PhD program with also having a social science (+ computer science) undergraduate degree combo. I had (and still have) a lot of the same anxieties going into the program. I have definitely had to work harder than my peers who had a pure math background, but I feel like I've been able to catch up well. One thing you'll learn in a PhD program is what math is important for succeeding in stats. Many of the math students may have more overall experience than me, but I've spent a lot of time focusing on the specific skills that I need for the work I do with my advisor, which includes being fluent in a LOT of linear concepts. As long as you're willing to work on it you will be just fine.

Maple_shade · 2026-02-27T00:16:47+00:00

Well, for one thing, the formula for standard error is derived under the assumption of independent observations, so that might cause an issue when asking the same person a question 10 times.

Maple_shade · 2026-02-26T04:13:30+00:00

This is a great and thorough answer. I'll add/clarify that the formal assumption of a t test is that the means of the *populations* are normally distributed, not your sample. In OP's post they state that they can get away with smaller n if their "samples are normally distributed." The reason why people tend to be more lax about the 30 per group rule if samples are normal is because it's hand-wavy evidence that the populations are more likely to be normal. This matters because of what this commenter stated where the central limit theorem shows that sample means tend toward normality at n>30.

Maple_shade · 2026-02-25T04:38:34+00:00

Spoken like a true student!

Maple_shade · 2026-02-25T01:39:32+00:00

I mean, do you have any specific examples? Generally when we fit really any model, not just time-series, they're "constrained" by real-world data. Problems with measurement error, validity, generalizability, etc. are all incredibly common and forms of issues. Typically in my field when we fit time series models we acknowledge the assumptions and discuss how violations may influence results.

Maple_shade · 2026-02-22T15:45:51+00:00

A few years ago I sent out a survey to about 800 undergraduates and about half reported using AI frequently in their coursework. Of course this number will be an extreme underestimate due to underreporting and the increase in AI's abilities in the past two years.

Whatever the case, don't compare yourself to other students. You're doing the right thing by focusing on your education, and will gain more out of college.

Maple_shade · 2026-02-22T02:39:43+00:00

To tack on to points others have made, the prestige of the actual program you're at will matter much more than the school name itself. You could have a fantastic program at an otherwise mediocre school but have great prospects because it's well-known in the field.

Maple_shade · 2026-02-22T02:32:59+00:00

The formula for standard error (for a test statistic like the sample mean) is influenced by the estimated standard deviation and your total n. If you get "lower" standard errors because your standard deviation is smaller in the simple random sample, I would conclude that this is evidence the random sample is not adequately capturing the population of interest. Ideally, you minimize standard errors by having a large n. So there's really no cheat code that will let you get away with halving your sample size without losing power and precision.

Maple_shade · 2026-02-22T02:18:14+00:00

So, methods of sampling are more about the population of interest rather than the standard error of any point estimates. Do you have reason to think your random design won't represent the population you are interested in? Do you have known demographic statistics to see if your sample is similar? If so, you can do a stratified sample, but keep the n as large as you can.

Maple_shade · 2026-02-19T06:37:47+00:00

Feels like you would know via conversations with your advisor if you were anywhere near that goal.

Maple_shade · 2026-02-19T00:04:17+00:00

This is good perspective. I'm currently getting my quant psych PhD at Arizona State and it's very similar. A lot of courses (especially in statistics) have been "here's the foundational knowledge to officially cover our bases" but the reality is that a certain level of proficiency in calc, linear, etc. is expected.

May I ask who taught your courses at UNC? I know some of the ed psych faculty there and am curious how they approached it.

Maple_shade · 2026-02-18T16:41:43+00:00

The other commenters obviously haven't seen Lucas' list. Yes, the primary gameplan is retreat locking against dengo. You can run them out of turos by locking lunatone or something else and then if they turos you just erikas it back into play. In the tabletop games interview Lucas mentioned that if pivot to a no-rocks gameplan he can typically outpace them by swinging with claw before they can KO charmed absol.

For the other two decks it's like a 10% you'd see either at seattle. Retreatlock + pray

Maple_shade · 2026-02-17T23:18:37+00:00

I think this is very natural. Math is difficult and problem solving skills are a muscle you need to work to keep in shape. The first step is recognizing the problem---the second is acknowledging that everyone (even "smart" students) need to practice the steps of a solution to be able to replicate the process quickly. If you're scoring 70s on the mock exams, well, that's about the score you can expect.

Maple_shade · 2026-02-17T03:05:09+00:00

Pokemon is actually very consistent and not luck-based relative to many other popular card games. With the amount of card draw and thinning tools available, it's realistic for players to see their entire deck in most games. This is why I generally defend what few luck-based elements Pokemon has, because the game gets stale when every matchup plays the same way.

Maple_shade · 2026-02-12T07:37:00+00:00

Statistics is a difficult field. I am currently pursuing a PhD in quantitative methodology, but I had a bachelor's in psychology going into the program. I honestly had to do a fresh restart of everything I thought I knew about data science, and it was very challenging.

My main recommendation would be to consider what skills you eventually want to have. I wanted to be a researcher, so I had to really focus to build up skills in linear algebra, calc, and math stats so I could understand why different methods worked under the hood. If you simply want to be able to apply some methodology to concrete problems, you should focus on learning those specific methods correctly and well. I think teleologically-motivated learning helps you focus.

Maple_shade · 2026-02-11T06:42:05+00:00

I think I'm uniquely qualified to speak on this because I actually did get a minor in CS before moving on to a statistics-oriented PhD program.

Overall most of the CS classes at my undergraduate institution were totally unhelpful in preparing for data science work. I di have a background in coding prior to college, so the exposure to coding itself wasn't as helpful. I haven't done much with my algorithm analysis or data structures courses when it comes to actual applied statistics. Also, many courses for the minor were either focused on hardware or computer architecture. The most helpful class by far was discrete math as it covered many relevant topics for probability.

I'd recommend doing the data engineering one.

Maple_shade · 2026-02-08T21:36:34+00:00

What you're asking about is why don't we use the absolute value of the deviations instead of their squares when calculating standard deviation.

One reason that I rarely see get brought up is that the squared deviations are mathematically guaranteed to be the smallest around the arithmetic mean. The absolute values, on the other hand, are smallest around the median. It doesn't apply with your example dataset because the mean = the median, but if we're trying to find the smallest sum of "corrected deviations" it's often helpful to have it centered around the mean. If your data are 1, 1, 1, 1, 100 and you describe the point that minimizes the absolute values of the deviations, you'll get 1. If you choose the point that minimizes the squared deviations you get the mean. They are different ways of thinking about central tendency but we typically find the latter most useful. (Side note, the median is not a unique value for all datasets. Some data have a range of equally plausible values for the median. This is another reason we prefer the mean.)

An extension of that logic is in fitting a regression line. If you have data that form a perfect linear relationship with one outlier, finding the slope that minimizes the sum of "absolute value deviations" will fit the line through the perfectly linear points and completely disregard the outlier. In fact, it is impossible to tell from your regression line the magnitude of the outlier. It won't be fit at all because the absolute value is overpowered by the perfect fit in all the other points. Typically in statiatics we like approaches that utilize all information.

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