Official LRC President Donald J Trump Thread

Flagpole wrote:

eric a blair wrote:

this is, of course, what sunk HRC - she was found blameless for the Benghazi losses, but during the investigation they found that she used her own email server.

I'm completely sure that Mueller is looking for evidence of financial crimes in the trump orgs. And since the trump org has admitted to many financial crimes, I'm sure they'll find more. The question is if congress will impeach him for cheating on taxes or money laundering.

Yep. I have days when I think that surely even the Republican congress can't allow this person to continue as President if they find financial crimes or collusion or more money laundering than they already know about, but then I see adults kiss his butt at the Secretary meeting and people who should know better (like Paul Ryan) continue to defend and support Trump, and I think that this circus will likely continue. Ryan has the least integrity of any Republican in the House. All big bad and mighty about not standing for Trump's behavior when he was still a candidate that most people thought would lose, but now that's President, everything Trump does is fine. Paul Ryan should be ashamed of himself, and I really hope he is.

eric a blair wrote:

It always amazes me when trumpers deny trump lies a lot. Are they serious? Do they seriously not know that he is a degenerate liar? Do they define truth as whatever trump says? Do they just like the way he talks? Does it 'sound' more truthful than when professorial Obama spoke?

The Post found trump to lie 4.9 times per day during his first 100 days. So throw out half of them. Fine. You still have more than two lies per day.

https://www.washingtonpost.com/news/fact-checker/wp/2017/05/01/president-trumps-first-100-days-the-fact-check-tally/?utm_term=.d53290e7b68a

Geffen Records Inc. wrote:

Ok then, list his twice a day lies, I'll wait for it.

Here's someone who actually slept in the same building that they did...unlike Flagpole who will lie like there's no tomorrow. But if Geffen said Trump was a liar, then he'd post it and it would be gospel. You lefties are pathetic liars.

Maureen Dowd quoted former Clinton supporter David Geffen as saying a variety of unkind things about the former first couple. "Everybody in politics lies, but they [the Clintons] do it with such ease, it's troubling," said the media mogul.

"It is ironic that the Clintons had no problem with David Geffen when he was raising them $18 million and sleeping at their invitation in the Lincoln Bedroom."

EPIC Flagpole wrote:

FP is a LOT smarter than you wrote:

LOL Good one, dude.

Now perhaps you can actually try to answer the question without any diversions (Oh wait, you're a Trumpette - that is simply not possible for you. Feel free to prove me wrong on this by actually answering the question).

WRT:

"3.3 is off of the actual result of 2.1 by a factor of 1.57 (or "57% larger than 2.1"). That is not at all "cold clear accuracy." The bulk of the results (the "+4") were off the actual outcome by a factor of 2. That is not good."

Why do you think that dividing by 2.1 is appropriate? Is 2.1 relevant to the scale of the numbers here?

Would being off by 1.2% (as in 3.3% vs 2.1%) be a whole lot better if the poll vs actual numbers were 7.2% vs 6.0% (maybe something like 3x better)?

What about if the poll number was 1.21% vs the final result of 0.01%? Should we then interpret this "inaccuracy" as being 1.2/.01 = 12,000% ? Wow, those would have been SO much worse polling results, no?

Go for it, dude. Let's see if you can actually answer the question being asked. I'd put the probability that you can and will at 0%. How can I be so confident? You're a Trumpette. Prove me wrong.

I'll continue to play because this is kind of fun...

"Why do you think that dividing by 2.1 is appropriate? Is 2.1 relevant to the scale of the numbers here?"

I think I actually understand why you'd be asking this question now (see next answer). Comparing the actual election outcome (2.1) to the polling mean (3.3) normalizes the data. Just referring to the difference in nominal terms doesn't reflect the scale of the error. "They were only off by 1.2" is misleading. This is very relevant within the context of the months and months of polling data at our disposal. This is where I believe you may not be making the connection: it's not as if we took a single poll and then had the election, having no idea where the actual result would end up. In that case, you could consider 2.1 and 3.3 to be pretty accurate. This is where the confidence interval comes into play. We had months and months of polling data that narrowed the expected outcome down to a narrow range.

"Would being off by 1.2% (as in 3.3% vs 2.1%) be a whole lot better if the poll vs actual numbers were 7.2% vs 6.0% (maybe something like 3x better)?"

First, the 3.3 and 2.1 (and 7.2 and 6.0 in your example) should not be percentages. I'll give you the benefit of the doubt, and assume this was an honest mistake.

You need to consider the sample distributions of the polls. In your new example, are most of the polls showing +9, +10, +11 margins, with an outlier at -2 or 0? Or are they all between +5 and +8? This matters. Confidence intervals correct for this.

But if the sample distribution maintains the same nominal precision, with the poll mean and respective actual outcome floating around in tandem, the accuracy essentially remains the same. And this is where I assume you don't like the normalization I applied initially?

FP is a LOT smarter than you wrote:

EPIC Flagpole wrote:

I'll continue to play because this is kind of fun...

"Why do you think that dividing by 2.1 is appropriate? Is 2.1 relevant to the scale of the numbers here?"

I think I actually understand why you'd be asking this question now (see next answer). Comparing the actual election outcome (2.1) to the polling mean (3.3) normalizes the data. Just referring to the difference in nominal terms doesn't reflect the scale of the error. "They were only off by 1.2" is misleading. This is very relevant within the context of the months and months of polling data at our disposal. This is where I believe you may not be making the connection: it's not as if we took a single poll and then had the election, having no idea where the actual result would end up. In that case, you could consider 2.1 and 3.3 to be pretty accurate. This is where the confidence interval comes into play. We had months and months of polling data that narrowed the expected outcome down to a narrow range.

"Would being off by 1.2% (as in 3.3% vs 2.1%) be a whole lot better if the poll vs actual numbers were 7.2% vs 6.0% (maybe something like 3x better)?"

First, the 3.3 and 2.1 (and 7.2 and 6.0 in your example) should not be percentages. I'll give you the benefit of the doubt, and assume this was an honest mistake.

You need to consider the sample distributions of the polls. In your new example, are most of the polls showing +9, +10, +11 margins, with an outlier at -2 or 0? Or are they all between +5 and +8? This matters. Confidence intervals correct for this.

But if the sample distribution maintains the same nominal precision, with the poll mean and respective actual outcome floating around in tandem, the accuracy essentially remains the same. And this is where I assume you don't like the normalization I applied initially?

OMG, did you really just say this:

"I think I actually understand why you'd be asking this question now (see next answer). Comparing the actual election outcome (2.1) to the polling mean (3.3) normalizes the data. Just referring to the difference in nominal terms doesn't reflect the scale of the error. "They were only off by 1.2" is misleading."

?1?1?1?

Holy shit, dude, you really don't have any idea what you are talking about. None. Zero. Zip.

You haven't been just kidding all along. OMFG.

And I have to ask again, are you really, honestly too stupid to see how inane that is? I guess the answer is yes.

Here's another clue for you dude (or should I call you dudette?): The actual margin of victory has no, nada, zilch, zippo relevance in "normalizing" the data. Zip.

Seriously, are you really too stupid to understand that? If not, if you have any brain cells at all, go back to a 1.201 projected margin of victory versus an actual margin of 0.001 and contemplate how the poll was off by 120,000%.

Seriously, are you that stupid? I simply cannot believe it.