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Self-Employment Questions Answered

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Self-Employment Questions Answered - Everything you wanted to know, and many things you didn't.
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jim_carson
2928 days ago
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Haha
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129. MARC MARON: The social media generation

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129. MARC MARON: The social media generation

Marc Maron is a comedian and the host of my favourite podcast, WTF with Marc Maron, which is a comedy podcast where Maron interviews not only comedians, but musicians, actors, chefs and artists. His conversations are always engaging, funny, raw and honest. I recommend it especially to those who are pursuing a creative field, as most of his interview subjects have insightful and unique stories about how they became successful. (As you can tell from its title, WTF contains explicit language and is for mature listeners … you’ve been warned!)

Maron’s own success story is worth mentioning. In his 40s, having lived a life of anger, resentment, addiction, failed relationships and burnt bridges, Maron had just gotten fired from a radio gig when he started the WTF podcast as a last, desperate attempt to stay in the comedy game. The podcast not only became incredibly successful, leading to a resurgence in his stand-up career and a television series, but it’s also proven to be his salvation.

I can’t believe it’s taken me so long to do a Maron quote, as I must have listened to hundreds of hours of his voice while working on Zen Pencils. This quote is taken from his latest memoir, Attempting Normal.

RELATED COMICS: Bill Hicks It’s just a ride, Louis C.K. We don’t think about how we talk, George Carlin On assassination (explicit), Henry Rollins Who’s the crazier man?.

- Since my last comic about social media, I think it’s fair to say I’m still totally dependent and addicted to my phone. Who checks their phone as soon as they wake up and while still in bed? I do. Who takes their phone into the toilet with them? Me. It’s gross, but I bet you do it too … don’t lie. Who can’t be alone in public without looking at their phone every five minutes? Yep, me again. While I love social media (it has obviously helped Zen Pencils enormously and it’s incredible how easy I can interact with readers from all over the world), we should also remember some of its negative side effects, as this article points out.
- What are your favourite podcasts? Some of my other recommendations: Hardcore History, The Bugle, The Smartest Man in the World, The Nerdist, Stuff You Should Know, StarTalk Radio and The BS Report.

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jim_carson
3212 days ago
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wow
schnuth
3208 days ago
Sad but true.
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How to Not Sell My Wife a Car

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The day has finally come! Asking the Wrong Guy now has it's own website! This week's columns will be posted on this site as well, but from then on, you should go to askingthewrongguy.com.

On the "Other Stuff" page there is a donation button. I only mention it because I know he won't.

Anyway, thanks everyone for helping us get this thing off the ground. It was killing me that Ric had no outlet for his brilliance, and now, thanks to all of you, he does.

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jim_carson
3267 days ago
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I think I've met this dealer :)
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Geek Productivity

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Pretty accurate. Some people just don’t understand that performing at a high level requires some rest periods in between the productivity. Or maybe we really are just lazy – I can’t say for sure.

image

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jim_carson
3337 days ago
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Yep. That's pretty much my day.
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About that p-value article… | Not So Standard Deviations

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Last night on twitter there was a bit of a firestorm over this New York Times snippet about p-values (here is my favorite twitter-snark response). While the article has a surprising number of controversial sentences for only 180 words, the most offending sentence is:

By convention, a p-value higher than 0.05 usually indicates that the results of the study, however good or bad, were probably due only to chance.

One problem with this sentence is that it commits a statistical cardinal sin by stating the equivalent of “the null hypothesis is probably true.” The correct interpretation for a p-value greater than 0.05 is “we cannot reject the null hypothesis” which can mean many things (for example, we did not collect enough data).

Another problem I have with the sentence is that the phrase “however good or bad” is incredibly misleading — it’s like saying that even if you see a fantastically big result with low variance, you still might call it “due to chance.” The idea of a p-value is that it’s a way of defining good or bad. Even if there’s a “big” increase or decrease in an outcome, is it really meaningful if there’s bigger variance in that change? (No.)

I’d hate to be accused of being an armchair critic, so here is my attempt to convey the meaning/importance of p-values to a non-stat, non-math audience. I think the key to having a good discussion of this is to provide more philosophical background (and I think that’s why these discussion are always so difficult). Yes this is over three times the word count of the original NYTimes article, but fortunately the internet has no word limits.

(n.b. I don’t actually hate to be accused of being an armchair critic. Being an armchair critic is the best part of tweeting, by far.)

(Also fair warning — I am totally not opening the “should we even be using the p-value” can of worms.)

by Hilary Parker

In the US judicial system, a person on trial is “innocent until proven guilty.” Similarly, in a clinical trial for a new drug, the drug must be assumed ineffective until “proven” effective. And just like in the courts, the definition of “proven” is fuzzy. Jurors must believe “beyond a reasonable doubt” that someone is guilty to convict. In medicine, the p-value is one attempt at summarizing whether or not a drug has been shown to be effective “beyond a reasonable doubt.” Medicine is set up like the court system for good reason — we want to avoid claiming that ineffective drugs are effective, just like we want to avoid locking up innocent people.

To understand whether or not a drug has been shown to be effective “beyond a reasonable doubt” we must understand the purpose of a clinical trial. A clinical trial provides an understanding of the possible improvements that a drug can cause in two ways: (1) it shows the average improvement that the drug gives, and (2) it accounts for the variance in that average improvement (which is determined by the number of patients in the trial as well as the true variation in the improvements that the drug causes).

Why are both (1) and (2) necessary? Think about it this way — if I were to tell you that the price between two books was $10, would you think that’s a big price difference, or a small one? If it were the difference in price between two paperback books, it’d be a pretty big difference since paperback books are usually under $20. However if it were the difference in price between two hardcover books, it’d be a smaller difference in prices, since hardcover books vary more in price, in part because they are more expensive. And if it were the difference in price between two ebook versions of printed books, that’d be a huge difference, since ebook prices (for printed books) are quite stable at the $9.99 mark.

We intuitively understand what a big difference is for different types of books because we’ve contextualized them — we’ve been looking at book prices for years, and understand the variation in the prices. With the effectiveness of a new drug in a clinical trial, however, we don’t have that context. Instead of looking at the price of a single book, we’re looking at the average improvement that a drug causes — but either way, the number is meaningless without knowing the variance. Therefore, we have to determine (1) the average improvement, and (2) the variance of the average improvement, and use both of these quantities to determine whether the drug causes a “good” enough average improvement in the trial to call the drug effective. The p-value is simply a way of summarizing this conclusion quickly.

So, to put things back in terms of the p-value: let’s say that someone reports that NewDrug is shown to increase healthiness points by 10 points on average, with a p-value of 0.01. The p-value provides some context for whether or not an average increase of 10 points in this trial is “good” enough for the drug to be called effective, and is calculated by looking at both the average improvement, and the variance in improvements for different patients in the trial (while also controlling for the number of people in the trial). The correct way to interpret a p-value of 0.01 is: “If in reality NewDrug is NOT effective, then the probability of seeing an average increase in healthiness points at least this big if we repeated this trial is 1% (0.01*100). The convention is that that’s a low enough probability to say that NewDrug has been shown to be effective “beyond a reasonable doubt” since it is less than 5%. (Many, many statisticians loathe this cut-off, but it is the standard for now.)

A key thing to understand about the p-value in a clinical trial is that a p-value greater than 0.05 doesn’t “prove” that NewDrug is ineffective — it just means that NewDrug wasn’t shown to be effective in the clinical trial. We can all think of examples of court cases where the person was probably guilty, but was not convicted (OJ Simpson, anyone?). Similarly with the p-value, if a trial reports a p-value greater than 0.05, it doesn’t “prove” that the drug is ineffective. It just means that researchers failed to show that the drug was effective “beyond a reasonable doubt” in the trial. Perhaps the drug really is ineffective, or perhaps the researchers simply did not gather enough samples to make a convincing case.

edit: In case you’re doubting my credentials regarding p-values, I’ll just leave this right here… statusbitchin

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jim_carson
3388 days ago
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Nice explanation. In line with the comment I read on another blog "I have a two--tailed coin. If I flip it three, it comes up tails all three times. That is consistent with the hypothesis that it is a normal coin. But it is not a normal coin. “The result is consistent with chance” is not the same as “the result is due to chance.” It just isn’t. But people often act as if it is, and that’s bad."
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