Link to thread on twitter: https://x.com/shylockh/status/1855872507271639539

Also a substack post in a better format: https://shylockholmes.substack.com/p/evidence-suggesting-voter-fraud-in

From my understanding, the user is arguing that the vote updates repeatedly favoring Democrats in Wisconsin were statistically improbable and uses p-values produced from binomial tests to do so. His analysis seems fairly thorough, but one glaring issue was the assumption of independence in his tests where it may not be justified to assume so. I also looked at some quote tweets criticizing him for other assumptions such as random votes (assuming that votes come in randomly/shuffled rather than in bunches). This tweet gained a lot of traction and I think there should be more concern given to how he analyzed the data rather than the results he came up, the latter of which is what most of his supporters were doing in the comments.

The commonly accepted "You're more likely to die driving to the airport than you are in the plane" orthodoxy just doesn't seem intuitive when you consider the annual number of car trips in Los Angeles county (where I live) and the annual number of deaths. So, I set out to do a fair (even with my admittedly predisposed bias) comparison for an individual living in Los Angeles.

First, the standard comparison that everyone quotes is fatalities per distance traveled. This immediately favors airplanes by inflating the denominator because planes travel so much faster and farther. I found on wikipedia (with a citation) that the airline industry uses "per distance" measure in press releases, but the insurers in the airline industry use "per journey". That's very telling.

I'm comparing GLOBAL air travel to driving in LA. Why? Commercial air travel is largely consistent globally. While there are some variations, probably mostly in where pilots train, it is a global system built on an idea of homogeneity. There are two dominant global plane manufacturers, Boeing and AirBus, plus smaller manufacturers of regional aircraft that also operate globally (e.g. Embraer, Bombardier), and Air Traffic Control is standardized. The 737 MAX crashes (as an example) could have just as easily been on Southwest Airlines in the US as they were in Ethiopia and Indonesia. I think many travelers are possibly as likely to fly within the US as they are internationally. I don't think country borders are very meaningful in assessing flight risk.

As for limiting driving to Los Angeles. 99+% of my car travel is within Los Angeles County. It doesn't seem logical to include driving stats for, say, Ethiopia in a statistical comparison relevant to someone not living in Addis Ababa. Additionally, unlike the homogeneity of air travel, car travel is absolutely not consistent globally. There are massive variations in commonly in-use autos, seatbelt use, road qualities, traffic enforcement, driver training, licensing requirements, etc etc.

So, with that:

Global airline trips: 36.5m per year (2015-2019 average. 2019 was the pre-COVID peak, so I started there and went backwards)

Global air traffic fatalities: 270 per year (2015-2019 average)

LA County car trips: 66m passenger and freight trips per DAY = 24.09b per YEAR (2018 data)

LA County traffic fatalities: 746 total - 265 pedestrians - 39 bicycles = 442 car and motorcycle deaths per year (2015-2019 average. Motorcycle deaths are 125.)

Ok, so with this data alone:

36.5m/270 = 135,185 flights per death (I'm putting trips in the numerator only to make the numbers more readable)

24.09b/442 = 54,502,262 car trips per death in LA County

Car travel in LA is 403x safer than global commercial air travel?

Something about this doesn't sound right. Should I be factoring in the number of passengers on the form of transport?? Similar to the point about planes flying farther and faster advantages the fatalities per distance metric, a car crash kills fewer people in one accident, favoring cars. So, I made up a measure called "passenger-trips" which (I think?) accounts for the number of people in the plane/car.

Global air travel passengers: 4.08b (2015-2019 average. Which works out to ~112 passengers per flight on average, which passes my smell test.)

Passengers per car: 1.5 (2019 US average, not LA specific)

LA County car passenger-trips: 1.5 * 24.09b = 36.135b

So:

4.08b/270 = 15,111,111 air passenger-trips per death

36.135b/442 = 81,753,393 car passenger-trips per death

Car travel in LA is 5.4x safer than global commercial air travel.

And this is for LA, the car capital of the world. Residents in other cities may see lower risk relative to flying. Conversely, I'd imagine that if you lived three hours south of LA in Tijuana (which I recall from my UCSD days was a driving free-for-all), you're probably better off getting on a plane. :)

Thoughts (especially on passenger-trips vs just trips)?

I just don’t get how the mode is gotten by tracing the intersection on the bar, is there any proof for this?

I'm good at math naturally but I've never yet taken a statistics class so please explain this without using formal math symbols or words that I might not know the meaning off.

Suppose I have the numbers 12345 then the average is 3 and then the average difference from the average is (2+1+0+1+2)/5 = 6/5 or 1.2

But according to the standard deviation, its sqrt((4+1+0+1+4)/5) which is root2 which is 1.41421356

GPT told me that the purposes are

1. To make it mathematically easier to work with (but I dont understand how or why that would be)

2. To make the biggest differences... even bigger (which I think is arbitrary and not necessary)

So the STD seems counterintuitive to me. Why is it used instead of just doing the method of (2+1+0+1+2)/5 ?

An independent variable in my multiple regression model has a very large (.60) p-value. Can I safely conclude that that variable has absolutely no bearing or influence on the outcome? Or is there simply always a possibility of a type 2 error? If I can't make such a conclusion can anyone please explain why?

Hello, as the title probably suggested, i need some help because, honestly i'm out of time and energy and I can't figure something out. I want to begin by saying I KNOW NOTHING about statistics (i'm a med student), but sadly i need to make a Kaplan-Meier survival curve and i can't seem to figure it out how to imput the data correctly. To give a bit of a context, I'm making a study with a group of about 35 people and i just wanna put into this graphic which one of them had/didnt have an infection at some point. I have for ALL of them the time (moment of diagnosis for the disease im researching - present day = no of months) but i cant seem to figure it out how to imput the data correctly. I tried it a couple of times with the help of chatgpt but it doesnt seem to work. Ive attached an image of WHAT I AM TRYING TO DO. please just help a girl out :(

I’ve been tracking how much I poop since 2023. This year, I have included the time and date of when I have pooped and with the data set that I have what could I analyze within it and how would I approach it?

1. Undisclosed Conflict of Interest (COI)

The research uses IELTS scores from "mock tests" conducted by The Forum English Center.

The Problem: The lead author, Nguyen Hoang Huy, owns this center.

He describes his own company as a "reputable organization" but fails to disclose his ownership.

This is a major ethical breach, as the paper acts as a commercial advertisement for his business rather than an objective scientific study.

2. The "Negative" Standard Deviation Impossible

In Table I, the paper reports a Standard Deviation of -0.67200 for exam scores.

The Reality: In mathematics and statistics, a Standard Deviation can never be a negative number. It represents a distance from the average, which is always zero or higher.

Conclusion: This is not a typo; it is proof that the table was filled with random numbers manually instead of being calculated from real student data.

3. Extremely Weak Prediction Power (Low R-Squared)

The paper reports an R-Squared value of 0.187.

The Reality: This means the IELTS score only explains about 18% of the student's final grade. The other 82% is completely unknown or random.

Conclusion: You cannot claim to have a "reliable conversion method" when your model fails to explain 82% of the data.

4. Wrong Use of "Cronbach’s Alpha"

The authors used a tool called Cronbach’s Alpha (Result: 0.701) to claim their data is "reliable."

The Reality: Cronbach’s Alpha is only for surveys and questionnaires (like "Rate from 1 to 5"). It is not used for comparing two independent test scores like IELTS and school exams.

Conclusion: This is "pseudo-science"—using a fancy-sounding term incorrectly to trick people who don't know statistics.

5. The Math Doesn't Add Up (Means vs. Equation)

The paper gives this formula: Final Grade = 5.843 + (0.501 x IELTS Score).

The Test: In a real model, if you plug in the Average IELTS Score (reported as 5.2844), you MUST get the Average Final Grade.

The Calculation: 5.843 + (0.501 x 5.2844) = 8.49

The Reported Grade: The paper claims the average grade is 9.12.

Conclusion: A gap of 0.63 points is huge in statistics. The formula and the averages come from two different, unrelated sets of fake numbers.

6. Correlation vs. Slope Mismatch

The paper claims a Correlation of 0.432.

The Reality: There is a fixed mathematical link between Correlation, Standard Deviation, and the Slope of the line.

The Calculation: Based on their reported Correlation and Standard Deviations, the slope should be 0.245.

The Paper's Claim: They reported a slope of 0.501 (more than double!).

Conclusion: This is the "smoking gun." The Correlation, the Standard Deviation, and the Regression formula were all made up separately and do not fit together.

Regarding Table I: How is it mathematically possible to obtain a negative Standard Deviation? If this is a "typo," could the authors provide the original raw dataset for independent verification?

Regarding Model Consistency: Why does the regression equation fail to intersect at the mean point of the data? A discrepancy of 0.63 points suggests the model was not derived from the reported statistics.

Regarding Parameter Mismatch: Why does the reported Slope (0.501) not match the value calculated from the reported Correlation and Standard Deviations (0.245)? These three values are mathematically inseparable; how did the authors arrive at such a large contradiction?

Regarding Ethical Disclosure: Why was the lead author’s ownership of "The Forum" English Center omitted from the paper, given that the data source is his own commercial entity?

Regarding Peer-Review: Were these fundamental mathematical errors raised by the reviewers during the submission process? How did an academic journal accept a paper where the basic descriptive statistics violate the laws of mathematics?

First of all I get the basic explanation, you choose 1 of 3 doors and the you have to choose if you change doors or not, if you don’t then you have your 1/3 chance of winning and if you change you have 2/3, it doesn’t matter if Monty opens the 2 or 3 door, as long as one of them has a price you win. It’s conditional probability I get it.

My confusion is in the fact that it’s obvious you can’y apply conditional probability here, you aren’t opening the door you choose so when Monty opens the second door that change of % should also affect the first door, in other words it doesn’t matter if you change or not.

Does it make sense? am I wrong? It looks obvious to me but no one else sees it, did I get something wrong, can someone explain it to me?

Genuine question- I can't figure out how to think about this, please help!

Starting with the assumption that a given child will be assigned one of two sexes (male, female) at birth, the probability of the child being female is 0.5.

A family has 5 female children. The probability of having 5 children assigned female at birth in a row is 0.5 x 5 = 0.03125.

A sixth child is on the way. Is the probability of the child being female 0.5 (because each child is an independent event) or 0.015625 (because it's the sixth outcome in a row)?

What AI tools can I use to statistically analyze data? Are there any tool where you dump an excel sheet and it analyses it?

As I understand it, there are many cows which are harvested from, but surely the eggs and milk aren’t transported too far lest they spoil, meaning they must come from local cattle. I live in Northern Illinois, so statistically speaking, judging by the higher population of cattle, how likely is it that I would be getting my eggs and milk from the same one?

I'd like to know if it's a good idea to study using ChatGPT, Copilot, or Gemini. I ask them to explain parts of the books we use in the class of Statistical Theory that I don't understand. Could you tell me if it's a good idea?

Hear me out, how many times does anyone rank anything less than a 5. I am sure there is some statistical jargon here to explain this but I feel as though the collective measurement for an “okay”, “acceptable” or “average” meal on a scale of 1-10 is a 7.

BTW I asked this in askreddit and they removed it saying I should try this sub, I found this funny and just decided to roll with it.

I want to be a market researcher or a data scientist, which is better stats or economics degree

Hi all,

The title is pretty much self explanatory; I got into those three “blue” institutions, and was wondering if any of you had any advice. For completeness, I got into a really top college at Oxford (one of Worcester, Magdalen and Christ Church), if that is relevant for postgrad life.

I don’t want to give too much detail on my research as I could possibly dox myself, but I’m originally from Europe and would like to work in the quant space in NYC after the PhD. The research opportunities seem best at Yale as the faculty is young and putting out cutting-edge research, but I’m also prioritising other things like well-being and making friends. Any thoughts would be highly appreciated!

Sou acadêmico de medicina e fiz um curso extensivo na internet sobre estatística, que me forneceu uma boa base
Me deparo agora com a escolha mais cruel nesse campo: qual software estatístico usar
Considerando que não tenho conhecimento sobre SPSS ou R, qual vocês me recomendariam?
Se possível, me recomendem um livro texto também

Hi All,

My name is Amarjeet(45M).

Please let me know how I can learn and grasp the basics concepts of Statistics.

I want to learn DS/ML.

Thanks in advance, Amarjeet

I work with someone that is from a quantitative field but not strictly statistics. They are presenting their results with models that I’m sure fail model checking tests and violate rules of independence (60% of the dataset are not independent of each other). However, they are finding ‘significant’ results aka p values in their favor so they presented it.

Have other people found themselves in this situation? A lot of work has been put in the analysis and presented to stakeholders. I’m trying my best to flag this while not stepping on toes

I took a 240 hour course to learn coding and R, where the professor was so conservative that we were not even using RStudio. Later, I completed a basic Python biostatistics course.

Now, eight years after I first started working with data analysis, I like to think that I understand the basics of statistics and R. I do not consider myself an expert, but I at least understand what I am doing and can recognize when an analysis does not make sense. I still use GraphPad for simple analyses, but I have started using R for more complex data analysis.

Back to the present, I use ChatGPT because, honestly, it saves me time. It can generate the entire code based on my needs, and I know enough to troubleshoot by guiding the AI. Because of that, I can automate analyses and build reproducible pipelines with reports much faster.

So why do people think that someone who already understands statistics and is starting with R should still learn coding from scratch and do everything themselves, instead of going straight to using AI?

Maybe this is a silly question to ask but I was wondering why statistics are always run in coding programs? It seems like an incredibly complicated way to do statistics especially for a biologist like me. They teach minimal coding in university. Why can't their be a program with UI where I can just click buttons like "run this data as a linear regression", or just click a button to get the average. If code already exists for all of these functions why can't it be made into an easier UI? Just let me click on a subset of my data instead of having to write an elaborate code to do that. Maybe i'm just salty I'm to dumb to understand code.

Loosing my mind over Rstudio 🙃

Hello Everyone,

I have to plan some things in my academic career. In order to do that I wanted to know if someone knew what the top universities are in Europe and the US for theoretical statistics PhD’s.

Thanks for taking the time