I am currently in my fourth year at university majoring in statistics and I remember stumbling upon and watching this video in my freshman year and being inspired about the importance of statistical literacy and a career in statistics. This video would be an inspiration again three years later, when I would write my personal essays for law school and write about the mistrial of Sally Clark. Just a couple of days ago, I was accepted to my top choice. It's funny how things work out like this.
It's not just statistics that juries aren't clued in to. My ex, a reporter who had the court beat, watched an innocent man go to prison for robbing a bank - the jury simply couldn't understand the simple pythagorean geometry, that, knowing where the bank robber was standing, and the height of the surveillance camera, that the defendant was a full six inches taller than the actual bank robber. Worse, her editor nixed any explanation of what went wrong - he said "nobody would get it".
I just googled Sally Clark. Unfortunately, it seems she never recovered from the trauma she suffered by the death of her children and her wrongful conviction, and she died a few months after this talk was given. :-(
When I saw the word "statistics", I thought it was wrong to be dull. But it certainly one of the best TEDtalk I have seen. Really important topic as well.
Well I think he was explaining that when you throw HT and you want HTT but you get HTH you already have the H for your next Trial (on the way to HTHTT). On the other hand, when you throw HT, looking for HTH, but getting HTT you have to start with an H again (on the way to HTTHTH, which is 6 cyphers)
Just a quick proof that it takes 8 flips on average for a HTT pattern: First of all, on average, it takes some amount of time to start our pattern. We know it takes about two flips on average to find a heads, which is the start of our pattern. Let's write this and continue: E[Flips until HTT] = 2 + ... Now we have a heads. There is a 50% chance we then get a second heads, which means our pattern is over. But at least we are back at the beginning of the pattern! And it didn't take two flips to get there, only one. So we subtract one from the same expectation we are studying. All together: E[Flips until HTT] = 2 + 0.5 * ( E[Flips until HTT] - 1 ) + 0.5 * ( ... ) The other 50% of the time, we are now on HT. Again, there are two scenarios: 50% of the time, we are done! That took two flips, where both flips did exactly what we wanted: E[Flips until HTT] = 2 + 0.5 * ( E[Flips until HTT] - 1 ) + 0.5 * ( 0.5 * ( 2 ) + 0.5 * ( ... ) ) The other 50% of the time, we are back to the same H beginning. Because it costed exactly two flips, we don't need to subtract anything: E[Flips until HTT] = 2 + 0.5 * ( E[Flips until HTT] - 1 ) + 0.5 * ( 0.5 * ( 2 ) + 0.5 * ( E[Flips until HTT] ) ) Re-arranging, we get: 0.25 * E[Flips until HTT] = 2 Solving, we get: E[Flips until HTT] = 8 --- Similar logic will get you the other expectation for HTH: E[Flips until HTH] = 2 + 0.5 * ( E[Flips until HTH] - 1 ) + 0.5 * ( 0.5 * ( 2 ) + 0.5 * ( E[Flips until HTH] + 2 ) ) E[Flips until HTH] = 10 As you would expect, most of the terms are the same. In the right-most term, you'll notice how we don't get to reset when we fail to hit the pattern on our final flip. This makes sense, since failing to get HTH towards the end of the pattern really messes you up because you're now seeking a heads from fresh all over again. Hope this helps! p.s. there are a few ways you could arrive at the same answer, but they all involve using this trick of feeding the expectation back into itself. I find expectation calculations where the original expectation appears again on the right side of the equation to be quite beautiful. Happens all the time in memoryless systems.
Another TED talk which shows useful math that should be taught in schools. Thank goodness I became an engineer. Otherwise my math education would have been predominantly pointless.
In a single flip-stream race, it is 50:50 as to whether the HTT-seeker or HTH-seeker will "win." However if the HTT-seeker and HTH-seeker uses a different flip-stream, the HTT seeker is likely to find his first. HTT first-occurrence-average = positions 6-7-8. HTH first occurrence-average = positions 8-9-10.
The first example is a word-game for a particular context: genetic dna strings, intertwined results. In real life, while we should look more often at how previous results influence future ones, it is almost always more useful to group events into distinct equally cardinal groups [HTT]vs[HTH] instead of [HTTHTH]vs[HTT]. The other example with the 99% faithful test results could very easily be summarized as: 1% error is a large number over a large population - 1 in 100 vs 10,000 in 1,000,000 and then factor in the rarity of the condition to begin with. I have a very rare condition, which makes it difficult for doctors to believe, but I point out that although rare, someone *must* have this condition, for it to be also true that so many others not have it.
Interesting talk. I happen to be doing a statistical project right now. The jury system would not be as bad as to get everything upside down wrong, if some statistician was also used as an expert. Everyone makes mistakes, and these mistakes are encouraged because we have trust in authority, so that when authority gets it wrong, we get it wrong too. I think statistics is beautiful in giving a general impression of fact, but whenever possible, not to rely on it.
I find this topic immensely fascinating, but more so, I find it fascinating that we are surprised mathematics is what is needed to make a correct determination. Physicists have said it for decades, mathematics is fact, witness testimony is a brain fooled by a magician or illusion.
Sorry, limitations of space here: [...] is higher in the HTT example. Because when you get HTH, you don't have the HTT pattern, but you only need two more to get it. Understand?
In his early example tossing coins, he says A) is the right answer, but then proceeds to prove that C) is the right answer. His explanation is clear: throwing HTHTH gives 2 chances to get HTH, while there is no way to throw 5 coins and get HTT twice. Therefore, C is true, not A.
Excellent. Of course, the perverse incentives purposefully caused by all other "systems" are worse than random juries. And, juries themselves are no longer proper juries. "Voir dire" is a new arrival (1850 in the USA), that lets the prosecutor pick juries that are biased in favor of the law, for instance. The same with the licensing of lawyers under the "BAR". Add false judicial instruction (1895) to that, and 'contempt of court', and juries are anything but "randomly inserted judgment".
I ran the simulation. The experiment is to flip until either HTT or HTH appeared. When that experiment was simulated 1,000,000 times the average number needed to find a "winner" was 4.99. And it was 50:50 (NOT 6:8 as claimed) as to which "won" the race. I will be glad to provide the visual basic code.
I just ran the simulation 1 000 000 times and found that on average it took 10.004338 to find the first HTH, and 7.995638 to get HTT. Which is very close to the numbers given in the video. The sequence of flipps could be shared between the two results, even though I didn't do it that way in my code, but in that case you need to keep going until you have found both HTH and HTT to be able to tell how long the average sequence needed to get the expected values are. It is fairly counter intuitive that it takes longer on average to get one of the sequences but the chance in a race is 50:50. In the race case it is fairly obvious that it should be 50:50 since the first two positions are the same and if that sequence of 2 comes up HT the next will give a winner regardless of outcome. He gives a short explanation as to why HTT has a lower average time it takes to find and it has to do with that in the sequence if you get HT it is 50:50 if you get a T and find the expected sequence, but if you don't you still have a H which is 1/3 of the way to HTT. On the other hand if you have HT and want to find HTH, if you get a T you are 0/3 of the way since you need to wait for the next H to be able to start your sequence. The HTT sequence is always partly done after the first H has been flipped, the current sequence will either be at H or HT, where as HTH is back to 0 if there is a string of at least 2 Ts.
Yeah no joke. I was "100% certain" in my mind that B was the right choice... just seemed to make since that once you have "HT" that there is a 50% chance for a "H" and 50% chance for a "T" (to complete "HTH" and "HTT" respectively), but yeah I didn't take into account the restart of the pattern if you don't get the pattern you're looking for... spooky how our minds don't work when in comes to statistics. And that we fall for the appeal to authority fallacy when it comes to statistics too...
Well, I'm also a statistician and built a model this morning to see. I found that the HTH was indeed higher by about 2, but for me it was about 8 and 6. I think that can be attributed to either him not remembering the absolutes but he did remember the difference, or the fact that my coin toss routine was not exactly 50/50. I relied on computer generated tosses, and computer random tosses are biased a bit. But, he's right that, on average, HTH takes longer to appear.
It's because there are more opportunities for it to occur. for example. if I tos hththththththththththththththt I get it about a million times but htt not a single time.
He's not wrong but to derive an analytical proof is difficult. A computer simulation and analytical derivation can be found here: www.gotohaggstrom.com/Fooling%20juries%20with%20statistics.pdf
@@bob15479 No, it's rather that HTH and HTT occur the same number of times. However HTH can be interlinked with itself: HTHTH is two occurrences in a sequence of length five. The same is not true for HTT. So HTT will be more spread out and therefore tend to occur earlier.
This man is SO smart that I feel smart just by understanding what he's getting at. A brief lecture like this is amazing AND free! Too bad that millions of people would rather watch a Lady Gaga video or a Chimp smoking a cigarette...
Statistics are to a lawyer what street lamps are to a drunk. They they lean on them without getting much illumination. I'd rather have this guy on my side at the trial.
i think that's a real possibility, but as we don't yet understand a greater order [outside of the feeling of being part of the 'great work' and a step in the evolution of everything] i think we're more animal than we understand, and should try harder to live in balance with our environment while taking steps to grow beyond the planet and set a goal that humanity can get behind.
You're wrong. A is correct. Just because the HTH can appear in clumps, doesn't mean that the average number of tosses before the pattern appears is higher.
the universe is full of chaos. and chaos is basically good. the human's inability- and i believe it is an artificial inability, grown out of some of the sillier parts of society- to deal with chaos and change will only breed problems. people need to relearn how to cope with life and float on the chaos.
Plus each individual juror can affect the opinion of other jurors, or one juror could rise to 'alpha juror' status and be backed by 'beta jurors' and make an honest dissenting opinion of another juror more and more likely to vanish. My choice of language and metaphors were a bit fuzzy, but hopefully you get the message.
Misleading? The entire talk was about how average joe doesn't understand statics, then gives an example of the most severe real world example possible about how an average person accidentally mislead and average jury.
If a doctor says 60 % cure rates means on average 60 percent of people with the particular cancer has been cured in the past and so be positive. If one is positive, 60 can become 70 % and on he way to cure . If negative, 60 can become 50 %. It all depends on how the patient deal with hope for better. So be positive..Doctor is not god but help one to get better but all down to patient to remain optimistic. I hope i have enlightend since life is precious.Statistics is not precise mathematics
About the funny-ness of the jokes, well, that's a personal opinion, but I do agree that the intro should've been a bit shorter. Remove out the "other person's shoe" etc. But.... The presentation is extremely interesting in my opinion :)
No, that is further ignorance, since that is what most of Statistics is about. Professor Donnelly, the speaker, a Statistician, explained that the Genome project studied what we have in common, and that the new study is about what is different. Description tells you what's the same about a group of data. Inference tells you what is different or special. There even does exist a specific definition for outliers. Sadly, few people, for instance you, actually bother to study Statistics.
I am surprised the statistics expert was not called instead of relying on paediatrician,who would have got his statistical interpretation wrong? nest time,if a doctor says statistically,one has to ask if ti isa backed by a statisitican?
If you say test gets it right 99% do you mean if the person has the disease there is a 99% chance the test returns yes or the previous and if the person does not have the disease the test returns false 99% of the time??? Did a single person go get a test or did you test like a million people got one positive result and said this person has the disease??? The order /way things are tested matters!!! put this into your question!!!
well, more logic could be helpful. possibly analysis of psychology by a team of professionals, with a wide range of knowledge could help the jurers. It may help reduce the potential for error, as it is obvious that statistics are quite good at triggering emotion. Some logical evidence to back up and explain the conclusions from stats could be used. I guess I am a little weary of statistics.
Example: Beck-Bornholdt, Hans-Peter and Hans-Hermann Dubben (2001): "Der Schein der Weisen - Irrtümer und Fehlurteile im täglichen Denken. [The illusiveness of the wise men - Falsity and misjudgement in daily life].
Jeffrey Blaise Thank you for reminding me; I forgot to write what it was, just like when I send someone an email and forget the attachment haha. For anyone who doesn't know what we're talking about, my original comment was just 5:32
Hey, this looks like it's from the same session as Steven Levitt's talk on children's car seat safety. He's an economist but I'm sure guys will find it interesting as well, it's based on statistics. /watch?v=um5gMZcZWm0
well, a lot more people are familiar with Lady Gaga but don't know this guy at all, so it is not surprising that they watch a lot more of Lady Gaga. Somehow I feel that you made some kind of statistical error, witch is ironic commenting on this video, but I can't quite make the link.
its not a bad speech, but it is a somehow 'old hat' for statistician what he is talking about. And there are also many 'for public use' written books about such effects of statistics and errors by interpreting statistical results.
You are missing the point of TED talks. It's not experts talking to experts, it's experts talking to everyone who didn't study that particular thing. Why the stats people in the comments are somehow triggered by this is just weird.
he took way to long to get to the subject of the talk, and also his jokes are not that funny. they were ok at the start, but at some point, just stop, and try to make what you're talking about interesting instead.
I'm not talking about you. I'm talking about the MILLIONS of people that would watch Lady Gaga INSTEAD of something educational. I watch Rolling Stones and ZZ Top Vids AND watch intellectual and science videos. You must know who I am talking about. The Millions of People who would NEVER watch a video like this one... Peace, Randy
I am currently in my fourth year at university majoring in statistics and I remember stumbling upon and watching this video in my freshman year and being inspired about the importance of statistical literacy and a career in statistics. This video would be an inspiration again three years later, when I would write my personal essays for law school and write about the mistrial of Sally Clark. Just a couple of days ago, I was accepted to my top choice. It's funny how things work out like this.
congratulations!
@@gellymesowski3869 I forgot about this post since it was a while ago, but thank you!
love it! congratulations!
It's not just statistics that juries aren't clued in to. My ex, a reporter who had the court beat, watched an innocent man go to prison for robbing a bank - the jury simply couldn't understand the simple pythagorean geometry, that, knowing where the bank robber was standing, and the height of the surveillance camera, that the defendant was a full six inches taller than the actual bank robber. Worse, her editor nixed any explanation of what went wrong - he said "nobody would get it".
I just googled Sally Clark. Unfortunately, it seems she never recovered from the trauma she suffered by the death of her children and her wrongful conviction, and she died a few months after this talk was given. :-(
In my opinion, one of the best TED talks. Wish they had more talks about statistics and probability.
اخوكم من برنامج الدحيح 😄😄
جاي من الدحيح 2 😂😂
الدحيح يرحب بكم 😂😂
أحلى سلام
When I saw the word "statistics", I thought it was wrong to be dull. But it certainly one of the best TEDtalk I have seen. Really important topic as well.
The Sally Clark case breaks my heart :(
This is literally my math homework (to watch this video)
Were you taking Stats 110 at that time?
This is my public speaking homework!
Same lol. I'm in 11th grade now for AP Stats and it's been 3 years since you took it. Wow, the education system just keeps on improving 🙄🙄
austin bevis So now I also come here for my homework.😄
ECE 306 :/
And still these mistakes are made everyday. I, for one, am happy that he shed light on the subject.
Replying to self can be fun.
His statistic is accurate. I ran a different simulation and indeed the average n at which HTT appears is 8 and HTH at 10.
My Eps master in university Mr. Haghi loves Sally's case. He keeps referring to it. Every session!!
Well I think he was explaining that when you throw HT and you want HTT but you get HTH you already have the H for your next Trial (on the way to HTHTT). On the other hand, when you throw HT, looking for HTH, but getting HTT you have to start with an H again (on the way to HTTHTH, which is 6 cyphers)
This needs to be in the "Everything you know is wrong" playlist.
#الدحيح يرحب بكم 😂
Wow, this was a great talk! I'm not into numbers much, but he put it so it was interesting and easy to follow. Very, very good lecture.
This was my statistics homework. Good talk.
Just a quick proof that it takes 8 flips on average for a HTT pattern:
First of all, on average, it takes some amount of time to start our pattern. We know it takes about two flips on average to find a heads, which is the start of our pattern. Let's write this and continue:
E[Flips until HTT] = 2 + ...
Now we have a heads. There is a 50% chance we then get a second heads, which means our pattern is over. But at least we are back at the beginning of the pattern! And it didn't take two flips to get there, only one. So we subtract one from the same expectation we are studying. All together:
E[Flips until HTT] = 2 + 0.5 * ( E[Flips until HTT] - 1 ) + 0.5 * ( ... )
The other 50% of the time, we are now on HT. Again, there are two scenarios: 50% of the time, we are done! That took two flips, where both flips did exactly what we wanted:
E[Flips until HTT] = 2 + 0.5 * ( E[Flips until HTT] - 1 ) + 0.5 * ( 0.5 * ( 2 ) + 0.5 * ( ... ) )
The other 50% of the time, we are back to the same H beginning. Because it costed exactly two flips, we don't need to subtract anything:
E[Flips until HTT] = 2 + 0.5 * ( E[Flips until HTT] - 1 ) + 0.5 * ( 0.5 * ( 2 ) + 0.5 * ( E[Flips until HTT] ) )
Re-arranging, we get:
0.25 * E[Flips until HTT] = 2
Solving, we get:
E[Flips until HTT] = 8
---
Similar logic will get you the other expectation for HTH:
E[Flips until HTH] = 2 + 0.5 * ( E[Flips until HTH] - 1 ) + 0.5 * ( 0.5 * ( 2 ) + 0.5 * ( E[Flips until HTH] + 2 ) )
E[Flips until HTH] = 10
As you would expect, most of the terms are the same. In the right-most term, you'll notice how we don't get to reset when we fail to hit the pattern on our final flip. This makes sense, since failing to get HTH towards the end of the pattern really messes you up because you're now seeking a heads from fresh all over again.
Hope this helps! p.s. there are a few ways you could arrive at the same answer, but they all involve using this trick of feeding the expectation back into itself. I find expectation calculations where the original expectation appears again on the right side of the equation to be quite beautiful. Happens all the time in memoryless systems.
Another TED talk which shows useful math that should be taught in schools. Thank goodness I became an engineer. Otherwise my math education would have been predominantly pointless.
That was a pretty good lecture. A little dry; that's the nature of statistics. But for the BMW ad at the end it'd get a thumbs up.
In a single flip-stream race, it is 50:50 as to whether the HTT-seeker or HTH-seeker will "win."
However if the HTT-seeker and HTH-seeker uses a different flip-stream, the HTT seeker is likely to find his first. HTT first-occurrence-average = positions 6-7-8. HTH first occurrence-average = positions 8-9-10.
The first example is a word-game for a particular context: genetic dna strings, intertwined results. In real life, while we should look more often at how previous results influence future ones, it is almost always more useful to group events into distinct equally cardinal groups [HTT]vs[HTH] instead of [HTTHTH]vs[HTT]. The other example with the 99% faithful test results could very easily be summarized as: 1% error is a large number over a large population - 1 in 100 vs 10,000 in 1,000,000 and then factor in the rarity of the condition to begin with. I have a very rare condition, which makes it difficult for doctors to believe, but I point out that although rare, someone *must* have this condition, for it to be also true that so many others not have it.
i got the disease and the jury examples..but im still confused about the coin toss example. anyone care to explain? thanks :D
Interesting talk. I happen to be doing a statistical project right now.
The jury system would not be as bad as to get everything upside down wrong, if some statistician was also used as an expert.
Everyone makes mistakes, and these mistakes are encouraged because we have trust in authority, so that when authority gets it wrong, we get it wrong too.
I think statistics is beautiful in giving a general impression of fact, but whenever possible, not to rely on it.
Sally Clark died in March 2007, she had serious issues getting over this ordeal in her life. Terrible case.
That's rather scary to think about! I wonder how many innocent people have been convicted because of errors in statistical reasoning.
yes, it is something so simple yet something most people do not take into account and simply assume
this is very interesting course about statistics ...i never thought about that in this way
I find this topic immensely fascinating, but more so, I find it fascinating that we are surprised mathematics is what is needed to make a correct determination. Physicists have said it for decades, mathematics is fact, witness testimony is a brain fooled by a magician or illusion.
This peter donnelly chap is a smart fella.
Watching this video reminded me of my inadequacies in mathematics.
Did you inlude this:
HTHTH
As two HTHs?
Likely
Sorry, limitations of space here: [...] is higher in the HTT example. Because when you get HTH, you don't have the HTT pattern, but you only need two more to get it. Understand?
In his early example tossing coins, he says A) is the right answer, but then proceeds to prove that C) is the right answer. His explanation is clear: throwing HTHTH gives 2 chances to get HTH, while there is no way to throw 5 coins and get HTT twice. Therefore, C is true, not A.
Excellent. Of course, the perverse incentives purposefully caused by all other "systems" are worse than random juries. And, juries themselves are no longer proper juries. "Voir dire" is a new arrival (1850 in the USA), that lets the prosecutor pick juries that are biased in favor of the law, for instance. The same with the licensing of lawyers under the "BAR". Add false judicial instruction (1895) to that, and 'contempt of court', and juries are anything but "randomly inserted judgment".
Because those judges and lawyers have the educational level of a social worker.
I had trouble understanding and he received applause so I'm going to assume he's a genius.. :)
Mickey Farley uhh.. thanks?
I ran the simulation.
The experiment is to flip until either HTT or HTH appeared. When that experiment was simulated 1,000,000 times the average number needed to find a "winner" was 4.99. And it was 50:50 (NOT 6:8 as claimed) as to which "won" the race.
I will be glad to provide the visual basic code.
I just ran the simulation 1 000 000 times and found that on average it took 10.004338 to find the first HTH, and 7.995638 to get HTT. Which is very close to the numbers given in the video. The sequence of flipps could be shared between the two results, even though I didn't do it that way in my code, but in that case you need to keep going until you have found both HTH and HTT to be able to tell how long the average sequence needed to get the expected values are.
It is fairly counter intuitive that it takes longer on average to get one of the sequences but the chance in a race is 50:50.
In the race case it is fairly obvious that it should be 50:50 since the first two positions are the same and if that sequence of 2 comes up HT the next will give a winner regardless of outcome.
He gives a short explanation as to why HTT has a lower average time it takes to find and it has to do with that in the sequence if you get HT it is 50:50 if you get a T and find the expected sequence, but if you don't you still have a H which is 1/3 of the way to HTT. On the other hand if you have HT and want to find HTH, if you get a T you are 0/3 of the way since you need to wait for the next H to be able to start your sequence.
The HTT sequence is always partly done after the first H has been flipped, the current sequence will either be at H or HT, where as HTH is back to 0 if there is a string of at least 2 Ts.
I just saw him last wednesday evening, talking about stats and genetics.
because they sponsor TED.... its on the end of all TEDtalks
Bloody brilliant!
i completly get wot he's saying but i understand nothin about it.. as in i can see he's completely right but its hard to get my head over it!
Yeah no joke. I was "100% certain" in my mind that B was the right choice... just seemed to make since that once you have "HT" that there is a 50% chance for a "H" and 50% chance for a "T" (to complete "HTH" and "HTT" respectively), but yeah I didn't take into account the restart of the pattern if you don't get the pattern you're looking for... spooky how our minds don't work when in comes to statistics. And that we fall for the appeal to authority fallacy when it comes to statistics too...
I do not get the first HTH/HTT example. Is he wrong?
The Sally Clark's case makes sense though
Well, I'm also a statistician and built a model this morning to see. I found that the HTH was indeed higher by about 2, but for me it was about 8 and 6. I think that can be attributed to either him not remembering the absolutes but he did remember the difference, or the fact that my coin toss routine was not exactly 50/50. I relied on computer generated tosses, and computer random tosses are biased a bit.
But, he's right that, on average, HTH takes longer to appear.
It's because there are more opportunities for it to occur. for example. if I tos hththththththththththththththt I get it about a million times but htt not a single time.
He's not wrong but to derive an analytical proof is difficult. A computer simulation and analytical derivation can be found here: www.gotohaggstrom.com/Fooling%20juries%20with%20statistics.pdf
@@bob15479
No, it's rather that HTH and HTT occur the same number of times. However HTH can be interlinked with itself: HTHTH is two occurrences in a sequence of length five. The same is not true for HTT. So HTT will be more spread out and therefore tend to occur earlier.
This man is SO smart that I feel smart just by understanding what he's getting at.
A brief lecture like this is amazing AND free! Too bad that millions of people would rather watch a Lady Gaga video or a Chimp smoking a cigarette...
randy95023
Statistics are to a lawyer what street lamps are to a drunk. They they lean on them without getting much illumination.
I'd rather have this guy on my side at the trial.
i think that's a real possibility, but as we don't yet understand a greater order [outside of the feeling of being part of the 'great work' and a step in the evolution of everything] i think we're more animal than we understand, and should try harder to live in balance with our environment while taking steps to grow beyond the planet and set a goal that humanity can get behind.
You're wrong. A is correct. Just because the HTH can appear in clumps, doesn't mean that the average number of tosses before the pattern appears is higher.
Most wouldn't. Infact, most wouldn't even know where to begin to investigate that claim.
Great TED talk on stats.
the universe is full of chaos. and chaos is basically good. the human's inability- and i believe it is an artificial inability, grown out of some of the sillier parts of society- to deal with chaos and change will only breed problems. people need to relearn how to cope with life and float on the chaos.
Plus each individual juror can affect the opinion of other jurors, or one juror could rise to 'alpha juror' status and be backed by 'beta jurors' and make an honest dissenting opinion of another juror more and more likely to vanish.
My choice of language and metaphors were a bit fuzzy, but hopefully you get the message.
@obliviousaa That would be missing an important part of the point.
@eldadevata
Unfortunately judges and magistrates aren't any better, at least with a jury you could get one clear thinker who might have an effect.
Superb video.
Quite interesting really, but the title is more or less misleading.
Misleading? The entire talk was about how average joe doesn't understand statics, then gives an example of the most severe real world example possible about how an average person accidentally mislead and average jury.
If a doctor says 60 % cure rates means on average 60 percent of people with the particular cancer has been cured in the past and so be positive. If one is positive, 60 can become 70 % and on he way to cure . If negative, 60 can become 50 %. It all depends on how the patient deal with hope for better. So be positive..Doctor is not god but help one to get better but all down to patient to remain optimistic. I hope i have enlightend since life is precious.Statistics is not precise mathematics
he reminds me of rupert giles from buffy.
About the funny-ness of the jokes, well, that's a personal opinion, but I do agree that the intro should've been a bit shorter. Remove out the "other person's shoe" etc. But.... The presentation is extremely interesting in my opinion :)
That extrovert/introvert joke was the best thing I've heard in awhile and I'm going to use it.
No, that is further ignorance, since that is what most of Statistics is about. Professor Donnelly, the speaker, a Statistician, explained that the Genome project studied what we have in common, and that the new study is about what is different. Description tells you what's the same about a group of data. Inference tells you what is different or special. There even does exist a specific definition for outliers. Sadly, few people, for instance you, actually bother to study Statistics.
I am surprised the statistics expert was not called instead of relying on paediatrician,who would have got his statistical interpretation wrong? nest time,if a doctor says statistically,one has to ask if ti isa backed by a statisitican?
Yes
If you say test gets it right 99% do you mean if the person has the disease there is a 99% chance the test returns yes or the previous and if the person does not have the disease the test returns false 99% of the time???
Did a single person go get a test or did you test like a million people got one positive result and said this person has the disease???
The order /way things are tested matters!!! put this into your question!!!
Yeah, that's TERRIFIC!!!
just have several statisticians do an analysis independently and see if they match up and have them explain the analysis as well.
well, more logic could be helpful. possibly analysis of psychology by a team of professionals, with a wide range of knowledge could help the jurers.
It may help reduce the potential for error, as it is obvious that statistics are quite good at triggering emotion.
Some logical evidence to back up and explain the conclusions from stats could be used.
I guess I am a little weary of statistics.
I nominate Peter as Judicial Stat Czar.
Holy shit this guy is a genius.
60% of the time it works everytime.
That...doesn't make any sense
Nopes, listen again starting at 6:50.
Example:
Beck-Bornholdt, Hans-Peter and Hans-Hermann Dubben (2001): "Der Schein der Weisen - Irrtümer und Fehlurteile im täglichen Denken. [The illusiveness of the wise men - Falsity and misjudgement in daily life].
So... what does it mean when a doctor says, "Your cancer has a 60% cure rate."? Never mind, I think I might not like the answer.
5:32 Heads Tails question/thought experiment begins
Dixon Adair ?? what is it
Jeffrey Blaise Thank you for reminding me; I forgot to write what it was, just like when I send someone an email and forget the attachment haha. For anyone who doesn't know what we're talking about, my original comment was just 5:32
Daniel Jackson!
Hey, this looks like it's from the same session as Steven Levitt's talk on children's car seat safety. He's an economist but I'm sure guys will find it interesting as well, it's based on statistics.
/watch?v=um5gMZcZWm0
well, a lot more people are familiar with Lady Gaga but don't know this guy at all, so it is not surprising that they watch a lot more of Lady Gaga. Somehow I feel that you made some kind of statistical error, witch is ironic commenting on this video, but I can't quite make the link.
its not a bad speech, but it is a somehow 'old hat' for statistician what he is talking about. And there are also many 'for public use' written books about such effects of statistics and errors by interpreting statistical results.
You are missing the point of TED talks. It's not experts talking to experts, it's experts talking to everyone who didn't study that particular thing. Why the stats people in the comments are somehow triggered by this is just weird.
gets good at 11:00
He models jeans. xD
My fucking hero.
Statistician Right now 🤣
Oooo. Big wordz. Me not no what think.
he took way to long to get to the subject of the talk, and also his jokes are not that funny. they were ok at the start, but at some point, just stop, and try to make what you're talking about interesting instead.
جيد جدا😁
omg! thats my friend's Dad, no jks!!!
Portuguese legends are not working properly, they are not placed at the right moment.
OMG it's HUGH GRANT!
My thoughts eactly!
الدحيح
Just tell them you're a Liar..Problem solved.
No, i believe you are wrong.
I'm not talking about you. I'm talking about the MILLIONS of people that would watch Lady Gaga INSTEAD of something educational. I watch Rolling Stones and ZZ Top Vids AND watch intellectual and science videos. You must know who I am talking about. The Millions of People who would NEVER watch a video like this one...
Peace, Randy
Insanity.
If you're innocent :-)
Hoi Mariska! :)
wow, a bang on the head!!
فيه حد فاهم حاجه يا دحايح 😂😂
Uber-nerd.....!
Jesus christ, what a boring talk. No wonder people run away when they hear the word statistician.
That was probably the most important TED talk I've ever seen. Perhaps you should spend some attention.
quite possibly the worst clip on youtube!!!
This was my statistics homework. Good talk.