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Fisher, laid out guidelines for designing experiments using statistics and probability as a way of judging results. As an example, he told the story of a lady who claimed to taste the difference between milk poured into her tea and tea poured into her milk. Fisher considered ways to test that. What if he presented her with just one cup to identify?

NARRATOR : Fisher proposed that a reasonable test of her ability would be eight cups, four with milk into tea, four with tea into milk, each presented randomly. The lady then had to separate them back into the two groups. Why eight? Because that produced 70 possible combinations of the cups, but only one with them separated correctly. If she got it right, that wouldn't "prove" she had a special ability, but Fisher could conclude if she was just guessing, it was an extremely unlikely result, a probability of just 1.

Thanks mainly to Fisher, that idea became enshrined in experimental science as the "p-value", "p" for "probability. You might conclude, in fact, that it's not a random process, that this is interesting to look at what else might be going on, and it passes some kind of sniff test. In other words, if you assume your results were just due to chance, you'd see them less than one time out of 20; not very likely. It could be quite insignificant. You could be detecting a very, very, very small effect, but it would be called, in the mathematical lingo, "significant.

Since they prefer to publish successes, and getting published is critical to career advancement, the temptation to massage and manipulate experimental data into a good p-value is enormous. There's even a name for it: "p-hacking. What may be more important is to remember what was originally intended by a p-value. If you do an experiment and find the result that is statistically significant, that is telling you, "that is an interesting place to look and research and understand further what's going on," not "don't study this anymore because the matter is settled.

In fact, there are few career incentives for it. Journals and funders prefer novel research. There is no Nobel Prize for Replication. Another solution to p-hacking and the overemphasis on p-values, may simply be greater transparency. NARRATOR : Statistics continues to play a fundamental role in science, but really, anywhere data is collected, you'll find statisticians are at work, looking for patterns, drawing conclusions and often, making predictions, though they don't always work out.

The Presidential Election of was a tough one for pollsters, the folks who conduct and analyze opinion polls. One pollster, on election night, gave him one percent. I would be very surprised if anything else happened. And I think that really left people feeling almost lied to, almost cheated by these numbers. And exactly how do people predict elections? One way, is just by asking people who they'll vote for. We can actually select something called a sample.

To see if the soup is right, you taste a teaspoon, not the whole pot. To test your blood at the doctor, they typically draw less than an ounce, they don't drain you dry. But in many circumstances, finding a representative sample is harder than it sounds. And so these green beads, down here, are going to represent people who are for it, and the red beads are folks who are against it.

PATRICK MURRAY : The beauty of randomization is that as long as you throw everything from your population into one pot and randomly pull it out, you can be sure that you're within a certain percentage point of the actual value that's in that pot. That depends on how much accuracy Talithia wants. One measure is "the margin of error," the maximum amount the result from the sample can be expected to differ from that of the whole population.

It's the plus or minus figure, often a percentage, you see in the fine print in polls. But there's also "confidence level. The confidence level tells you how sure you can be about your result. A 90 percent confidence level means, on average, if you ran your polling or sample times, 90 of those times, it would be accurate within the margin of error. Talithia knows the total number of beads is a thousand.

And she's settled on a plus or minus five percent margin of error at a 90 percent confidence level. That means she needs a sample size of at least beads. We got a red beads and green, so about 48 percent of our population would vote against and about 52 percent would vote for. Now, remember that margin of error that we talked about, that plus or minus five percent?

So, once you take that into account those numbers really aren't that different at all. So, I guess you could say this puppy is too close to call. There were an equal number of red and green beads in the jar. While the sampling error built in from the mathematics can be quantified, there are other errors that can't. PATRICK MURRAY: The other parts of the error-how we word our questions, how the respondents feel that day, the responsibility to predict what their behavior is going to be somewhere down a line-all those sources of error are something that we can't calculate.

A few decades back when just about every household had a landline, finding a random sample meant randomly dialing phone numbers. If we randomly chose a phone number, somebody on the other end of that phone would pick it up and would do the interview with us. Thanks to Caller I. Response rates are way down.

So, you're kind of crossing your fingers and hoping the people you reach are the same as the ones that are actually going to vote. For example, we found in , pollsters were not reaching enough white voters without college degrees. But if you look at the polls themselves, and not the headlines, on average, polls on the national and state level were off by historically typical amounts.

NATE SILVER: So, when I hear people say, "Oh, the polls were wrong," then it probably reflects people's interpretations about the polls being wrong, where people, for various reasons, looked at the polls and they said, "These numbers prove to me that Clinton's going to win.

Trump's victory depended on fewer votes than the seating capacity of some college football stadiums, spread across three states: Pennsylvania, Wisconsin and Michigan. And there were some problems with the polls in those states that led to underestimating Trump's support, according to a postmortem by a consortium of pollsters.

Nate Silver, the founder of the website, FiveThirtyEight, is one of the biggest names in polling, even though he doesn't generally conduct polls. NATE SILVER: Our job is to take other people's polls and to translate that in terms of a probability, to say, basically, whether…who's ahead, which is usually pretty easy to tell, but then how certain or uncertain is the election is the more difficult part.

On the morning of Election Day , he gave Clinton about a 70 percent chance of winning and Trump about a 30 percent chance. That's like rolling a ten-sided die, with seven sides that are Clinton and three that are Trump. NATE SILVER: People who make probabilistic forecasts, they're not saying that politics is intrinsically random, they're saying that we have imperfect knowledge of it, and that if you think you can be more certain than that, you're probably fooling yourself based on how accurate polls…other types of political data are.

While a 30 percent chance might seem slim, if you learned the flight you were about to board crashed three out of every 10 trips, would you get on the plane? We can say, like, "It just did. It might have rained, it might not have rained, as it happened, it did. But in at least one field, there are literally decades of detailed statistics on how the contests played out: baseball.

Baseball has always been a game of numbers, box scores, batting averages, E. And for us, it's future player performance. Stuck with the low payroll of a small market team, he abandoned decades of subjective baseball lore and committed the organization to using statistical analyses to guide the team's decision-making. NARRATOR : Billy's stats-driven approach started to attract attention when the Oakland A's finished in the playoffs in four consecutive years and set a league record with 20 wins in a row.

Today, every major league baseball team has a sports analytics department, trying to predict and enhance future player performance through data, analyzing everything from the angle and speed of the ball coming off the bat to which players should be brought up from the minor leagues or traded.

I just understand its powers and its application. When you run a major league baseball team, which is a great job and every kid who dreams of doing it, I can tell you it's everything you've thought of. But when they ask me, "What do I have to do to do that? Proponents of data-driven decision-making and prediction have applied the approach to areas as diverse as popular music and law enforcement.

Moneyballing has been enabled by the vast amounts of information gathered through the internet, so-called "big data. But what about the opposite situation, when there's very little data, yet actions need to be taken, for example, when searching for people lost at sea? How do you even begin to predict where they might be? The U. Coast Guard's Sector Boston Command Center: from this secure set of rooms, the Coast Guard coordinates all operations in the Boston area, including national security, drug enforcement and search and rescue.

Fleming speaking. The Coast Guard initiates a search with a foot response boat…. A reason for that is in , I think we had deaths nationwide, and a number of those people that were recovered were recovered without a lifejacket. But with all the unknowns-where he got into trouble and how he may have drifted-the search area could be as large as 20 square miles.

Search and rescue operations are often based on unique circumstances and require action despite incomplete information. To attack problems like that, statisticians turn to an idea that originates with an 18th-century English clergyman interested in probability, Thomas Bayes. Imagine you are given a coin to flip, and you want to know if it is fair, heads or tails, or weighted to land more on heads than tails.

The traditional approach in statistics and science doesn't assume either answer and uses experiments to find out. In this case, that involves flipping the coin a lot. Or you could approach the problem like a Bayesian. Unlike traditional statistics, that means starting with an initial probability based on what you know. In this case, all the coins you've ever come across in a lifetime of flipping coins have been fair; seems likely this one is probably fair, too.

Next, you also flip the coin, updating the probability as you go. Let's say it starts off with several heads in a row. That might make you wonder, increasing your probability estimate that it's weighted. But as you flip it more times, those start to look like chance. In the end, your best estimate is that it is probably a fair coin, but you are open to any new information, like it belongs to your uncle the con man, Crooked Larry.

NARRATOR : …along with the readings of currents and winds, and combines them with information about how objects drift in the water to simulate thousands of possible paths the target may have taken. These get processed into probabilities, indicated by color and turned into search plans to be executed.

It does a lot of the calculations for us and provides us with a lot of valuable search patterns and search-planning options. A boater calls in a sighting from earlier in the day. The area has shifted about three miles farther out to sea.

Hours earlier, the paddleboard was placed in the water by another Coast Guard ship and allowed to drift. The instruments mounted on it are there to measure wind and record the path it's taken, information that will later be used to tweak the drift simulations in SAROPS, though the system performed quite well today.

And fortunately, because of stuff like this, we do get to make that call. But today's computing power has unleashed it on the world. It's in everything from your spam filter to the way Google searches work to self-driving cars. Some even find in the Bayesian embrace of probability, similarities to how we learn from experience. And they've built it into computers, making it part of a powerful new force: machine learning. No one teaches a child to ride using a set of rules.

There may be some tips, but ultimately, it is trial and error, experience that's the instructor. So, similar to like a small child that falls down and learns from this experience, we just let the computer learn from examples. By scanning through thousands of labeled pictures, some cats, some not, the computer can develop its own guidelines for assessing the probability that a picture is a cat.

At Stanford, we recently ran a study to understand whether a machine-learning algorithm can compete with top-notch Stanford-level board-certified dermatologists in spotting things like skin cancer. And lo and behold, we found that our machine-learning algorithm, our little box, is as good as the best human doctor in finding skin cancer. Should we trust software over our doctors? Or are diagnostic programs like Sebastian's the intelligent medical assistants of tomorrow, a new tool but not a substitute?

Mathematically, everything checks out just fine. The problem is that people massively underestimate just how often bad runs happen. Most gamblers, particularly those who never look deep into any betting related mathematics, would assume that in almost every case a result such as seven or eight in a row losses would be the worst expected run. Could you imagine a roulette wheel landing 10 or more times in a row on red?

Well, it does happen, and a lot more frequently than you expect. The other issue is the huge sums required to keep the system going, and not to mention that fact that it might get difficult to get your money on in the first place if the bet is massive. Imagine using this system for horse racing where you bet on the favourite every time.

It sounds unlikely that a series of races would extend by more than a few until a favourite won, but again, it does happen frequently enough. We wish you luck! If you try the Martingale strategy out, a couple of things are guaranteed to happen. Firstly you will experience some massive streaks of good fortune, and you also receive a vivid wake-up call about the reality of how often your luck can turn sour and for how long.

Before you try any kind of new betting strategy, you should do a test using play chips. Here are some points to consider if you harbour ambition to one day be a professional poker player. After an intense two weeks of hard-fought poker the Venom Series is over and a new champion has been crowned.

You've been idle for more than 3 minutes. Click, press the button bellow or any key to dismiss. Not Signed Up yet? Sign Up Now! Already have an Account? Login Now! History and How it Works So what is the Martingale betting strategy and where did it come from? Games to Use the Martingale Strategy on The classic example for the Martingale system is betting on red or black on a roulette table.

Blackjack, baccarat, and craps are all suitable as well. Issues with the Martingale Strategy The principles of the Martingale betting system are as sound as you could wish for. Summary If you try the Martingale strategy out, a couple of things are guaranteed to happen. Want poker news as it breaks? Join the Poker News and Gossip Telegram channel! Did you like this article? Articles Joined PokerTube November Mark from Stamford in the UK is a professional cash game player, and part time journalist.

A massive chess fan and perpetual traveller. He also produces strategy content for our sister wesbite PokerVIP. Read more.

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