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Joe Biden is now POTUS

ugk said:
My guess is that they view Texas as a luxury, and don't want to repeat the mistake of Hillary Clinton not solidifying Pennsylvania, Wisconsin, and Michigan. But yeah, given the sizable cash on hand they should at the very least be putting up ads there.
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Makes a lot of sense, agreed on both counts.
 
Just came across this on Facebook

122181470_203639637965711_1428169780650450219_n.png
 
playahaitian said:
Its interesting

Polling and "math" took a major hit after the last election results

Do trust the numbers MORE now?

What changes have been made in data collection accuracy and analytics since the last election?
Click to expand...

Because of the last election, it seems people are more skeptical, which they should be. Skepticism negates being complacent, which in turn makes people take more initiative.

The numbers are nothing more than predictions based on certain analytics. That’s why there are multiple organizations that collect data that gives different numbers.

This is literally the same exact thing we do with weather forecasting. When people see the weatherman giving a forecast, they are watching his predictions based on multiple numerical forecast model results. Are they 100% accurate? Certainly not. But yet people expect them to be 100%.

People was seeing Hilary with something like a 75% chance of winning and got comfortable. Because 75% mean more than likely, but yet, there is still that 25%, which the 25% happened.

Nothing has changed with the data collection and analytics. What has changed is the people’s attitude towards those polling numbers.

Even with Biden up big in the numbers and historical voting on the forefront, he ain’t guaranteed to win. People still have to do their part and not solely trust the numbers.
 
4 Dimensional said:
What's impressive is the number of independent ballots is only 6,712 less than the Republican ballots.
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Yeah but those folks lean one way (democrat) or the other (republican).
4 Dimensional said:
Because of the last election, it seems people are more skeptical, which they should be. Skepticism negates being complacent, which in turn makes people take more initiative.

The numbers are nothing more than predictions based on certain analytics. That’s why there are multiple organizations that collect data that gives different numbers.

This is literally the same exact thing we do with weather forecasting. When people see the weatherman giving a forecast, they are watching his predictions based on multiple numerical forecast model results. Are they 100% accurate? Certainly not. But yet people expect them to be 100%.

People was seeing Hilary with something like a 75% chance of winning and got comfortable. Because 75% mean more than likely, but yet, there is still that 25%, which the 25% happened.

Nothing has changed with the data collection and analytics. What has changed is the people’s attitude towards those polling numbers.

Even with Biden up big in the numbers and historical voting on the forefront, he ain’t guaranteed to win. People still have to do their part and not solely trust the numbers.
Click to expand...
Yet we saw a different trend in 2018 elections. Republicans are on the downtrend when it comes to winning.
The good poll numbers are making people overconfident. It's encouraging people to get out in vote.
 
4 Dimensional said:
Because of the last election, it seems people are more skeptical, which they should be. Skepticism negates being complacent, which in turn makes people take more initiative.

The numbers are nothing more than predictions based on certain analytics. That’s why there are multiple organizations that collect data that gives different numbers.

This is literally the same exact thing we do with weather forecasting. When people see the weatherman giving a forecast, they are watching his predictions based on multiple numerical forecast model results. Are they 100% accurate? Certainly not. But yet people expect them to be 100%.

People was seeing Hilary with something like a 75% chance of winning and got comfortable. Because 75% mean more than likely, but yet, there is still that 25%, which the 25% happened.

Nothing has changed with the data collection and analytics. What has changed is the people’s attitude towards those polling numbers.

Even with Biden up big in the numbers and historical voting on the forefront, he ain’t guaranteed to win. People still have to do their part and not solely trust the numbers.
Click to expand...

www.nytimes.com

Wrong or Imprecise? Understanding the Polls in the 2016 and 2020 Elections (Published 2020)

In this math lesson, students will use two fundamental statistical concepts — bias and noise — to analyze what went wrong in the 2016 presidential polls and evaluate the reliability of the forecasts for 2020.
www.nytimes.com www.nytimes.com

Wrong or Imprecise? Understanding the Polls in the 2016 and 2020 Elections
In this math lesson, students will use two fundamental statistical concepts — bias and noise — to analyze what went wrong in the 2016 presidential polls and evaluate the reliability of the forecasts for 2020.




Far fewer voters are telling pollsters they are undecided in this election cycle compared with 2016.Credit...Brendan Smialowski/Agence France-Presse — Getty Images
By Dashiell Young-Saver
  • Oct. 22, 2020


    • 2
Students in U.S. high schools can get free digital access to The New York Times until Sept. 1, 2021.
Lesson Overview
Featured Article: “The Upshot on Today’s Polls” by Nate Cohn
President Trump’s victory over Hillary Clinton in the 2016 presidential election was a surprise to many. Numerous pre-election polls showed Mrs. Clinton leading in battleground states like Michigan, Wisconsin and Pennsylvania.
After those forecasts underestimated Mr. Trump’s support in 2016, many Americans are approaching 2020 with a new mantra: “Don’t trust the polls.”
So, this election cycle, you may be wondering: Can we trust the polls in 2020? Can we trust polls ever again? In this lesson, you will use two fundamental statistical concepts — bias and noise — to analyze the 2016 polls. Then you will apply what you learned to evaluate the reliability of the 2020 election forecasts.
Warm Up
Do you ever look at polls — political or otherwise? Have you ever participated in one? Do you think they are useful or accurate measures of public opinion? Do you think they are given too much attention during election season?
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Continue reading the main story


The polls we’re talking about in this lesson are political polls — surveys of small samples of likely voters. We use them to estimate how the whole population will vote on Election Day.
Respond to the following questions about polls in writing, or in class discussion:
  • Why do you think polls use a small sample of likely voters? Why not try to measure everyone who will vote on Election Day?
  • Which poll would you trust more: one that sampled five likely voters or one that sampled 500? Why?
  • Which poll would you trust more: one that sampled individuals from many different backgrounds (for example, people of different races, genders, education levels and political affiliations) or one that mostly sampled individuals from one type of background? Why?



Image
Credit...Dashiell Young-Saver
In the above image, imagine the bull’s-eye is the true percentage of people who will vote for President Trump in your state (something we’ll know only after the election). The “x’s” are estimates provided by polls. Respond to the following questions:
  • Which target illustrates the best polling? Which target illustrates the worst polling? How do you know?
  • Based on the image, describe what you think it means for polls to be accurate. Describe what it means for polls to be precise. What is the difference between accuracy and precision?
  • Which is more important: accuracy or precision? Why?
The statistical term for accuracy is bias. The term for precision is noise. Here is the same graphic using those terms:



Image

Credit...Dashiell Young-Saver
How do these terms make you think differently about accuracy and precision, if at all?
Pollsters spend their careers trying to reduce bias and noise in their polls. In general, they reduce bias by polling sets of individuals that are representative of the whole population. They reduce noise by collecting a greater sample size to have more data.
Activity Part I: What Happened in 2016?
In a 2017 article breaking down polling errors from the 2016 election, Nate Cohn discusses the practice of weighting polls to provide better estimates:
Education was a huge driver of presidential vote preference in the 2016 election, but many pollsters did not adjust their samples — a process known as weighting — to make sure they had the right number of well-educated or less educated respondents.
Click to expand...
If pollsters use good weighting, they make their samples more representative of the actual population, which reduces bias.



In 2016, highly educated voters preferred Mrs. Clinton by a large margin. In addition, highly educated voters respond to polls more often. National polls adjusted for these trends by weighting their samples. Use the graphic below, from Mr. Cohn’s article, to study the effect of weighting:

Respond to the following questions:
  • How would you explain the meaning of weighting in polls in your own words? Why do unrepresentative samples create bias?
  • In 2016, what effect did weighting of education have on the estimated proportion of Trump voters?
  • In 2016, various polls in key states, especially those in the Midwest, did not use weights for education levels. Why was this a problem? Does this problem lead to bias or noise? How do you know?
  • If you were a pollster in 2020, how might you avoid the mistakes of the 2016 election?
Activity Part II: What About 2020?
State polling methods have changed since 2016. Mr. Cohn writes about these changes in “Are State Polls Any Better Than They Were in 2016?”:
Another source of polling error was the failure of many state pollsters to adjust their samples to adequately represent voters without a college degree. Voters with a college degree are far likelier to respond to telephone surveys than voters without one, and in 2016 the latter group was far likelier to support Mr. Trump. Over all, weighting by education shifted the typical national poll by around four percentage points toward Mr. Trump, helping explain why the national polls fared better than state polls.
Four years later, weighting by education remains just as important. The gap in the preference of white voters with or without a college degree is essentially unchanged, despite the appeal Mr. Biden was supposed to have with less educated white voters.
In the New York Times/Siena College surveys conducted in October, Mr. Biden’s combined lead over Mr. Trump in the core six battleground states — Wisconsin, Pennsylvania, Michigan, Arizona, Florida and North Carolina — was two percentage points. That lead would have been six percentage points had the polls not been weighted by education or turnout (which correlates with education).
Although they could still be doing better, more pollsters are weighting by education today than four years ago. Over all, 46 percent of the more than 30 pollsters who have released a state survey since March 1 appeared to weight by self-reported education, up from around 20 percent of battleground state pollsters in 2016.
Some of the increase is because a handful of pollsters have decided to start weighting by education, a prominent example being the Monmouth University poll. But more of the change is because of the high volume of state online polls, which have always been likelier than state telephone surveys to weight by education.
Click to expand...
Now, read the entire article so that you can apply what you’ve learned to the 2020 election in the activities below.
1. First, explore the polls. Take five minutes to navigate the 2020 poll breakdown for today in “The Upshot on Today’s Polls.” Start by taking a look at the most recent polls in the left column. Then explore the polls in the right column, including those headlined: “A snapshot of current polling averages”; “Exploring Electoral College outcomes”; and “How polling averages have changed.”
As you examine the polls, reflect: What do you notice about the polls on this page? What do you wonder about any or all of the polls?
2. Next, focus on “A snapshot of current polling averages,” the first poll on the right side of the page. This snapshot has three categories: “Polling leader”; “If polls were as wrong as they were in 2016”; and “If polls were as wrong as they were in 2012.”
Answer the following questions:
  • By “wrong,” do you think it refers to bias or noise? How do you know? Why would that make a difference?
  • Compare the numbers from the “Polling leader” column with the numbers in the 2016 column. Which column is more favorable for Mr. Trump? Why do you think this is?
  • Do you believe the polls have adjusted enough to provide an unbiased picture of the 2020 election? Why or why not?
3. Finally, reflect on what you learned:
  • What did you learn about the uses and abuses of polls? Give at least three takeaways from this lesson.
  • What questions about polls and polling do you still have?
  • Does the lesson change how you think about polls? Will you trust polls more or less now?
  • What advice would you give to others who might be distrustful or confused by the current election polling?
Going Further
Option 1: Analyze and interpret a poll.
Dig deeper into The Upshot’s 2020 Poll Breakdown. Explore key states and how their polls have changed over time. See if political events, such as the first presidential debate on Sept. 29, seem to change poll numbers. What do you think could explain these changes?
Analyze poll quality. FiveThirtyEight rates pollsters based on various metrics. Explore their ratings, the metrics they use and how their metrics test for bias and noise. What stands out to you? What questions do you have?
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Continue reading the main story


Option 2: Make a prediction.
Based on the poll numbers, predict the election results. FanSchool offers an election challenge, where you can submit your Electoral College predictions.
Here are some other leading polling services to help you with your prediction:
FiveThirtyEight
Real Clear Politics
Quinnipiac University
CNN
Option 3: Conduct your own poll.
Apply what you have learned by creating your own poll.
Who will you vote for in the 2020 election? What’s your favorite pizza topping? How would you rate your online learning experiences?
The subject and questions are up to you — but whatever the poll topic, ask enough people for you to feel confident in your estimates. But be sure to consider critical questions explored in this lesson like bias, noise, weighting and sample size.
Start with the goals of your poll and what you want to find out. Then formulate your question or questions. Next, decide on a way to conduct your poll — via social media, phone or a free polling app like Google Forms. Finally, determine how you will display your findings (a chart, a graph or another format). You can create your visual representation by hand or by using a free design app like Canva.
Afterward, reflect on the process and your findings: How well were you able to reduce bias and noise in your poll? How might you conduct it differently if you were to do it again?
 

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