Chance, luck, and ignorance: how to put our uncertainty into numbers - David Spiegelhalter

We all have to live with uncertainty. We attribute good and bad events as ‘due to chance’, label people as ‘lucky’, and (sometimes) admit our ignorance. In this Oxford Mathematics Public Lecture David shows how to use the theory of probability to take apart all these ideas, and demonstrate how you can put numbers on your ignorance, and then measure how good those numbers are.
David Spiegelhalter was Cambridge University's first Winton Professor of the Public Understanding of Risk. He is the author of 'The Art of Uncertainty: How to Navigate Chance, Ignorance, Risk and Luck' (Penguin, September 2024).
David would like to add: 'When I was asking the audience to compare birthdays and phone numbers with their neighbours, I am deeply embarrassed to admit that some of my sums were wrong. In an audience of 200, I incorrectly claimed there were around 400 pairs, whereas of course it is only around 200. So I was very lucky to get so many matches."

[@richardfield6801]

This was so gratifying. I learned the rules for calculating probabilities mechanically in secondary school because I was sitting for a maths exam, but I always felt uncomfortable about them and could never quite accept their reality. Now, 55 years later, for the first time I hear a statistician underlining my doubts and telling me that many of us find it unintuitive and difficult. It's not just me then. Yay!

[@Hernespa99]

I love watching lectures at night to relax and eventually sleep and continue where i doze off the nxt ́night turns out it was so engaging and passionate and interesting that i fear i ll might lack some sleep 😅

[@ScienceAppliedForGood]

It was a very entertaining talk about chance and probability.

[@jonathanbirchley]

Nice lecture, fascinating and entertaining. Something I thought he might have explored, since the the first (double-headed coin) and last (10 heads in a row) tricks, is how our perception of likelihood is influenced by normally presumed, but not necessarily true conditions. Not only how likely something is, but what likelihood really means. We typically assume things like the coin having a head and a tail, and the video clip not being just a small slice of a long sequence. Often known as "default assumptions", they can easily cause us to be suprised or fooled. A well-known example is the infamous Monty Hall game-show problem.

[@Brockles2115]

This guy was on Total Wipeout ❤️❤️

[@CedricLatessa]

I know what I want for Xmas 📖

[@real_pattern]

certainly not...

[@horacioguillermobrizuela4295]

Just... amazing! I thought I knew something about probabilities...

[@richarddavis2605]

Thanks, I want to learn more!

[@dj.non.trivial]

that was an hour of my life I have a 100% probability of not getting back.

[@ke1007]

Both interesting and accessible, this put me in mind of my own 'remarkable coincidence'. In 2008 my dad died at the age of 86, and for the last week of his life he was on a palliative care ward. The man occupying the next bed was born on the same day (ie same day, same month, same year), we know this because the nurses queried it, fearing that there had been an admin error. Both men also died on the same day. We all commented at the time "what are the odds?", but many times since I've wanted to answer the question properly, to put a figure on it. Can anyone shed any light on this? I also wonder if their proximity at the time of death is relevant to the probability, or simply the circumstance which allowed us to know of their shared birth and death dates. If they had been born in the same village (they hadn't), would this make it even more improbable? Any views?

[@Bernie-k8n]

This reminds me of a first day question in a high school probability class question from our teacher. "How many of you, do you believe, have the same birthday?" Most would think about 365 days in a year , about 25 students etc. and believe it a long shot. It turned out that 3 groups of 2 had same date of year birthdays. It also was shown to us as the hour progressed that a calculated probability was that at about 22 random persons in a room it was a 50% probability. that at least two did have the same date of year birthday. So, what you might ask yourself is this. In all the hospital rooms, say in the state where you live, what is the probability that two people don't end up with same date of birth and same date of death over some day, week, month? Your situation, although unlikely for any one individual to experience, would not be an uncommon one, given that hospitals are where many die and old people are who usually die...

[@liam_dublin101]

Very enjoyable 😀👏

[@connectmetohacker4263]

That's nice that everything is matching like miracle. Maths predict the miracles

[@peterwusc]

Looking at the mug I am "almost certain" that MI5 agents are required to stay silent when the % falls in the gaps...

[@jonsmith7718]

Did the coin counting take into consideration that the 'tosser ..lol' stops as sooon as they get a tails - they don't complete all 10 tosses, so the time taken needs to take a average of the diminishing rate?

[@jennifereagleton5567]

I am an applied linguistics, not a math person, but this is interesting...

[@ianbromelow5640]

I once actually got 6 double-yolkers from a standard box of six eggs, not very long ago!

[@dschai0220]

The relationship between the perfect numbers and mersenne numbers. 6 / 2^1 (2) = 3 28 / 2^2 (4) = 7 496 / 2^4 (16)= 31 8128 / 2^6 (64)= 127 33,550,336 / 2^12 (64 * 64) = 8191 8,589,869,056 / 2^16 (64 * 64 * 16) = 131,071 137,438,691,328 / 2^18 (64 * 64 * 64) = 524,287 ...

[@mariamanos5461]

Why the chance if getting heads 3 times in a row is 1/8 but 3 children to be born same date in a row is not 1/365 to the 3rd power

[@gitgudnga]

because u havent asked the same question there are 365 days so the chance all 3 are born on jan 1st is 1/365^3 the same with jan 2nd and jan 3rd ….. 365 times so u add all the probabilities for each day to get 365*1/365^3 = 1/365^2

[@brunocombelles59]

Because in one case the expected result is specific (head) whereas in the second case, the day of birth is not relevant (i.e. the birth of first child does not play any part in getting a positive outcome or not, whereas the first throw, if it's tail, will fail the whole attempt) The chances of three coins flips getting the same result (irrespective of which) is 1/4. The chances of 3 children of being born on a specific day is 1/365 to the power of 3.

[@Pedritox0953]

Great video! Peace out

[@aristotledixit]

Probability does not exist, except for Russian roulette.

[@StephenLawrence-re8ub]

I think probability is objective because we can test it and see with enough repeat experiments. Probability relates to what would happen if we did such a test. Sports men and women and commentators know this, they talk in terms of how many times they would make that shot out of 10.