Primary Debate Scheduling and Fairness

There is an idea floating around that the Democratic primary debate schedule is deliberately designed to prevent anyone from realistically challenging Hillary Clinton for the nomination.  The number of debates is relatively small, and debates have been scheduled at times of poor television viewership.  This is in clear opposition to the Republican debate schedule which has more events and is scheduled at more favorable times.

I don’t have the tools to decide if this is a deliberate effort on the part of the DNC, but I can try to measure what effect the different debate schedules have had on the candidates opportunity to present their views.

Debate Number and Timeslots

Most obviously, the Democrats have 6 debates scheduled, and the Republicans have 13.  13 is more than 6.  But that’s a pretty simplistic analysis.  Much of the discussion in the media has centered on when the debates are scheduled.  The Democrats have a Tuesday, a Wednesday, a Thursday, two Saturdays, and a Sunday.  Weeknights typically have higher TV viewership than Saturdays, but Sunday is often better still (the Sunday in question is after a long weekend, however, which may make it less ideal.)  Republicans have two Tuesdays, two Wednesdays, four Thursdays, a Friday, and two Saturdays (and one unscheduled but planned debate).

So, the Republicans three of twelve badly scheduled debates is less than the Democrats two of six.  If the Sunday debate is in a ratings hole, the Republicans clearly scheduled better.  If it is a bright spot, they may be equivalent.  It’s hard to say right now, but time will tell.  In any case, the Democrats aren’t winning back any points the lost in the number by the scheduling.

Candidate Facetime

However, there is one place where the Democratic debates are doing a much better job than the Republicans, which is giving plenty of time to each candidate.  In the first Republican debate, only Trump edged out ten minutes of speaking time, whereas Hillary Clinton had over half an hour in the Democratic debate, and Bernie Sanders nearly so.  This trend has continued over the debates so far.

Now, this is obviously because there are far fewer Democratic candidates than Republican ones.  But that fact was at least somewhat known when the debate schedules were designed, so it can’t be ignored.

Eyeball-Minutes

To combine these various factors into a measurement of how helpful the debates have been in allowing a candidate to express their views, I will create a new number; the eyeball-minute.  One eyeball-minute represents the attention of one debate viewer for one minute of speaking time.  Actually, it represents one million debate viewers for one minute, for ease of calculation.

So, by combining the published television ratings for the debates with compiled lists of speaking times, I can calculate a sum total number of eyeball-minutes that each candidate has earned in the debates so far.  This uses the average television rating of each debate, where I could find it, and the peak rating where I could not.  It includes both main and secondary debates on the Republican side, but only for those candidates who have qualified for at least one main debate.

1. Donald Trump (R) – 1212
2. Hillary Clinton (D) – 1059
3. Ted Cruz (R) – 984
4. Jeb Bush (R) – 959
5. Marco Rubio (R) – 934
6. Ben Carson (R) – 865
7. Bernie Sanders (D) – 839
8. John Kasich (R) – 826
9. Carly Fiorina* (R) -821
10. Chris Christie* (R) – 798
11. Ron Paul (R) – 767
12. Martin O’Malley (D) – 619
13. Mike Huckabee* (R) – 585
14. Scott Walker** (R) – 334
15. Jim Webb** (D) – 239
16. Lincoln Chafee** (D) – 141

A single asterisk means that the candidate participated in at least one secondary debate, and a double asterisk indicates that the candidate withdrew from the race and so missed subsequent debates.

Returning to our original question, Bernie Sanders collection of 839 eyeball-minutes places him in an equivalent position to the majority of the Republican field.  Martin O’Malley’s 619 is lower, but not catastrophically so.

The eyeball-minute may not be the best measure of debate impact, but it has the advantage of being objectively measurable, and by that measure, the Democratic debates are by and large just as effective candidate message platforms as the Republican debates.

There are many debates to come, and I will hopefully return to check on these numbers again.

Shared Names and Voter Fraud

RedState.com contributor streiff wrote an analysis of a Fox News story about database matching of North Carolina’s voter roles.  The original story, while somewhat alarmist, at least acknowledges that the analysis is preliminary at best. What North Carolina did was compare a complete list of everyone who voted in NC in 2012 to a list of voters in 28 other states.   35,750 voters matched first name, last name, and birthdate.  765 voters also matched the last 4 digits of their social security number. The Fox News article points out that these aren’t guaranteed cases of voter fraud, but streiff ignores this, with his headline “Rampant Vote Fraud Uncovered In North Carolina”, and comments like this:

This is not a minor problem, this is an industry. Under a most favorable scenario one has to expect the overwhelming majority of the voters matching name and date of birth are the same person.

I don’t think we can accept that claim without some analysis.  The match on matching in large groups is a funny, and most people don’t judge it very well.

Birthdays In A Room

The classic example of this is to ask “How many people need to be in a room before there is a 50% chance that two of them share a birthday?”.  Most people think that, with 365 days to choose from, it would need to be in the hundreds. The actual answer is 23.  The first person to enter the room can have any birthday.  The second person has one day they cannot have.  The third has two, and so on.  While the chance for each person colliding with someone else as they enter the room is low, the cumulative chance that every person will dodge every other person shrinks faster than most people think.

The Null Hypothesis and Bias

It’s been a long time, and this is actually a boring subject, but it’s something I wanted to talk about.

Last week Nate Silver of FiveThirtyEight wrote a piece about flawed statistical thinking in an op-ed by Peggy Noonan.  He used some simple calculations to show his point.

Dan McLaughlin of RedState had an issue with Mr. Silver’s piece.

Silver concedes of his statistical analysis that “this calculation assumes that individuals’ risk of being audited is independent of their political views,” which of course is the very thing in dispute; it’s like the old joke about an economist stranded on a desert island with a stack of canned goods whose solution begins, “assume a can opener.” All things being equal, all things are equal.

Mr. McLaughlin fundamentally misidentified what Silver was doing when he made that assumption.  It doesn’t weaken his argument; it is necessary to make it, statistically.

The Null Hypothesis

What Mr. Silver was doing in his piece was using an informal version of the null hypothesis, which is the foundation of much of modern statistics.  The fundamental mathematical principle behind statistical significance relies, not on proving a hypothesis, but on disproving the null hypothesis.

Thus, if a statistician wants to show that smoking causes cancer, he does the math assuming that smoking has no effect on cancer.  If the math leads to an unlikely result, he has disproven the null hypothesis.   If the math doesn’t, then he has failed to disprove the null hypothesis.  A statistician never proves a positive hypothesis, they simply disprove null hypotheses.

This is a bit tough to grasp, so I’ll try to explain it with a very simple example.  If I have a coin that I think might be weighted, the way I test that is to flip if a bunch of times and write down the results.  Then, I assume it was 50-50 heads tails, and ask, “If the coin weren’t biased, how unlikely would it be that I got the results I just did?”  In 100 flips, if my sample came out 47-53, sure, the most likely answer is that it is slightly weighted.  But the null hypothesis is still very likely, so I would not reject it.  If my sample were 82-18, however, that would be a staggeringly unlikely event with a fair coin, so it is probably safe to reject the null.  If I only flipped the coin twice, however, I couldn’t disprove the null even both flips were heads, since that has a good chance of happening regardless.  Mathematically, this is what is represented by the p-value; the probability of a result like the one in question, given the null hypothesis.

That an individual’s risk of audit is independent of their political views is a null hypothesis.  Mr. Silver proposes it, then shows that Peggy Noonan’s evidence does not disprove it.  Thus, statistically, Peggy Noonan has very weak evidence.  He does this by showing that, if the null is true, it would not be unusual to find four or five (indeed, four or five thousand) Republican donors that were audited.   So, the fact that Peggy Noonan did find four or  five Republican donors that were audited is not statistical evidence that the null is false.

The null hypothesis itself, however, is not a political statement.  It is simply the way one has to formulate the problem in order to use the mathematical tools available.

As a side note, I was banned from RedState some time ago for formulating a statistical query in this way, because the null hypothesis looked like a political position, so the fact that it has come up again is of some interest to me.

Unionization and Economic Growth

Today we look at the following article on RedState, which purports to show that

…Without cherry-picking data as union bosses must in order to defend forced unionism, total seasonally adjusted non-farm employment growth shows a huge advantage for residents of right to work states.

The actual data presented, however, are the employment growth over 20 years for all right to work states, but only a few union-friendly states.

This, particularly considering that the introduction explicitly calls out cherry-picking, triggered my sensors.  So, let’s see what happens if we look at data for all states.

First, I have to find the data used to create this chart.  It took some rummaging on the BLS website, but I finally found numbers that almost, but not quite, recreate the numbers on the chart in the original article.  My version of the chart is below.

As you can see, it looks essentially like the version in the article, though because I am using slightly different data, the percentages vary by a point or two.

Now, I’d like to present a different chart, this time comparing Ohio to other union-friendly states.

Here is where the cherry picking comes in.  Ohio job growth isn’t terrible because it is union friendly, it’s terrible because it’s terrible.  Nearly everyone does better than Ohio regardless of their labor policy.

Now, there is potentially something to be said that right-to-work states have a greater gain over the last 20 years than union friendly states do.  But that wasn’t the argument; Jason hart asserted a “huge advantage” for right-to-work states, and presented evidence that was built around comparing to the second-worst performing state of any kind.

Presenting the data like that, particularly in the same sentence as calling out others for cherry-picking data, is disingenuous at best.

Evidence of Better Employment in Red States?

Speaking on CNN (and as reported at talkingpointsmemo), Governor Bob McDonnell, in an effort to link the economic recovery to Republican governors and not President Obama, said:

 “There’s something going on with Republican-governed states. Seven out of the 10 states nationwide, Candy, that have the lowest unemployment rates: Republican governor states.”

By now you should have figured out the drill; does this show real evidence that Republican-governed states have lower unemployment than Democratic-governed ones?

The short answer is no. The long answer is nnnnnnooooooooooooooooooooooooooooooooooooooooooo.  (sorry, bad joke). Simply put, there are more Republican governors than Democratic ones, so more Republican states appear in every part of the unemployment list.  Only 3 out of 10 of the lowest unemployment states are governed by Democrats, but only 3 out of 10 of the highest unemployment states are (the one independent governed state means that only 6 of them are Republican).

Of course, we can do better than just counting from the top 10 and bottom 10.  A simple statistical model can give a much better sense of whether governor party affiliation affects unemployment.  The answer is no.  While Republican governed states have slightly lower average unemployment, the difference is tiny (0.3%) and is very likely caused by random change (p=0.54).  Trying to refine the model by adding length of incumbency or length of party incumbency does not produce any results other than noise.  Sometimes one party is a little ahead, sometimes the other, but the results are never significant.

The conclusion is pretty clear, then.  Mr. McDonnell’s statement is factually true only in the most technical sense, and any implication he tries to draw from it is faulty.

Pay Equality at the White House

An article on The Free Beacon here makes the simple claim that the White House pays women less than men, according to public records.  They then go on to imply (as others who link to them do more explicitly, like here) that this is demonstrative of an anti-woman attitude in the administration.

So, lets take a look at the numbers, shall we?

Constitutional Models for Stable Democracy

A few months ago, Supreme Court Justice Ruth Bader Ginsburg was discussing the drafting of a new Egyptian Constitution in Egypt, and she said that she didn’t believe that the US Constitution was the best model.  This terribly offended many commentators, as can be seen here, here, and here.

But could there be a good reason why Justice Ginsburg doesn’t think the US Constitution is a good model?  I think there is; Presidential systems do not lend themselves well to long-term stable democracies, which is the goal of a well-written Constitution.  Of course, the United States is the exception, but how well do other countries with a Presidential system fare?

Political Sex Scandals Revisited

Some of you may recall that last summer, I tried to build a model to predict the results of political sex scandals, and documented my efforts here and here.  The model was unusual, and it turned out to predict the then-current sex scandal (David Wu) very poorly.

Well, another sex scandal has made the news, so it’s time to put my model to the test again.  Hermain Cain’s scandal isn’t very interesting; quite frankly.  The variables that matter to the model are pretty straightforward; Mr. Cain’s scandal is nothing special.

• Intensity: 5 – multiple instances of sexual advances, but no actual sex.
• Unfaithfulness: 7 – Cain has been married for 40+ years, but hasn’t quite been accused of actually cheating on his wife.
• Kinkiness: 3 – Nothing more than a little dirty talk.
• Hypocrisy: 4 – Courting the religious right but having adulterous intentions.
• Coercion: 6 – The actions were non-consensual.

The other ratings (such as Contrition, which is 1 (Cain denies the events), and Plausibility, which is 6 (there isn’t very strong evidence that they happened), aren’t a part of the model.

So, as a low intensity Republican with a coercive but not kinky scandal, the model does not predict a happy outcome for Mr. Cain.  Specifically, the result is a value of 0.16, which means he will most likely drop out of the race or lose the nomination.  But, this is the same model that predicted that David Wu wasn’t going anywhere on the precise day he announced his resignation, so take that with a grain of salt.

It should also be noted that only one of my model data cases (Jack Ryan) was a non-incumbent candidate for election, so the dynamics may be very different.  But I have the model, so it’s worth testing it again.  And the best way to test is to make the prediction in advance of the event, so there you are.

Border Security Revisited

My first real posts on this site were a 3 part series about border security, my loyal readers may recall.  As you may recall, in part 3 of that series, I assumed that nobody was seriously discussing putting lethal deterrents on the U.S.-Mexican border (like minefields and electric fences), both for political reasons and for reasons of basic human dignity.

How wrong I was.  Herman Cain recently declared his plan to build an electrified fence on the border to keep out illegal immigrants.  He later said he was joking, but his description of the plan is lacking in humor.

It’s going to be 20 feet high. It’s going to have barbed wire on the top. It’s going to be electrified. And there’s going to be a sign on the other side saying, ‘It will kill you — Warning’

By now, you probably know how this works.  For the moment, let’s assume Mr. Cain was serious, and let’s set aside the issues of killing illegal immigrants in cold blood.  How much would a fence like this cost?

The Fallacy of the Base Rate

This isn’t an analysis up to my usual standards, but I have been struggling for content these last few weeks.  Nothing out there seems to fit the somewhat particular requirements I have for a subject to write about; that it be a factual statement which requires aggressive or unorthodox analysis to get to the bottom of, but that there is a path of analysis to enlighten.  So, today I have a much simpler error, from wnd.com.

Writing about restrictions on unpasteurized milk, Bob Unruh observes:

The reason cannot be safety, the report said, since a report from the Weston A. Price Foundation revealed that from 1980 to 2005 there were 10 times more illnesses from pasteurized milk than from raw milk.

Unfortunately, this is citing a report (without giving details or linking to it) that cites another report that makes this claim.  I eventually found the original report here, only to discover that it has almost no further details about what it means.  From that report, we learn that there were 41 outbreaks and 19,531 illnesses attributed to pasteurized milk, and this is 10.7 times the illnesses for raw milk.  If you use the raw milk numbers later in the report, it comes out to 8.4 times, but they may be using different numbers.   It sounds damning, until you realize that this does not mean that an illness is ten times more likely from pasteurized milk.  The problem is that more people drink pasteurized milk, so even if illness is less likely, there will be more in total.