so I’m gonna try to see how it goes so the study behind it was published August
the study behind it was published August
the study behind it was published August 2015 in a psychologist conducted a
2015 in a psychologist conducted a
2015 in a psychologist conducted a hundred exact replications published
hundred exact replications published
hundred exact replications published studies only 36 of the replicates itself
studies only 36 of the replicates itself
studies only 36 of the replicates itself that is the same statistically
that is the same statistically
that is the same statistically significant results the first even worse
significant results the first even worse
significant results the first even worse only 47 of them thoughts the phone in
only 47 of them thoughts the phone in
only 47 of them thoughts the phone in the night confidence intervals for the
the night confidence intervals for the
the night confidence intervals for the original studies now if the original
original studies now if the original
original studies now if the original specs were done correctly because 95 of
specs were done correctly because 95 of
specs were done correctly because 95 of the reputations to follow the
the reputations to follow the
the reputations to follow the comprehensible because that’s what a
comprehensible because that’s what a
comprehensible because that’s what a confident civil is the only 47 did other
confident civil is the only 47 did other
confident civil is the only 47 did other studies to show fairly certain age in
studies to show fairly certain age in
studies to show fairly certain age in other scientific fields the larger
other scientific fields the larger
other scientific fields the larger problems have come in psychology and
problems have come in psychology and
problems have come in psychology and medicine which is especially worried so
medicine which is especially worried so
medicine which is especially worried so this seems like a pretty big problem for
this seems like a pretty big problem for
this seems like a pretty big problem for science a lot of science this is how
science a lot of science this is how
science a lot of science this is how many things in a fix-it lady larger
many things in a fix-it lady larger
many things in a fix-it lady larger sample sizes permutation of trials make
sample sizes permutation of trials make
sample sizes permutation of trials make stricter criteria for p-values all of
stricter criteria for p-values all of
stricter criteria for p-values all of these ideas and more are worth exploring
these ideas and more are worth exploring
these ideas and more are worth exploring the theme of this accident is
the theme of this accident is
the theme of this accident is questioning the north I will argue that
questioning the north I will argue that
questioning the north I will argue that the probably goes deeper than just
the probably goes deeper than just
the probably goes deeper than just specific research as alternative
besides busy these continues with Bayes
besides busy these continues with Bayes theorem here it is so the first term is
theorem here it is so the first term is
theorem here it is so the first term is T of the key given me this is the
T of the key given me this is the
T of the key given me this is the probability assuming that our hypothesis
probability assuming that our hypothesis
probability assuming that our hypothesis is true that you can get we’re doing
is true that you can get we’re doing
is true that you can get we’re doing science this is how confident we are in
science this is how confident we are in
science this is how confident we are in our hypothesis before we go to our
our hypothesis before we go to our
our hypothesis before we go to our second Nexus pap this is the probability
second Nexus pap this is the probability
second Nexus pap this is the probability that we get the other to be done so we
that we get the other to be done so we
that we get the other to be done so we put all these steps together it’s our
put all these steps together it’s our
put all these steps together it’s our lot P of each giving me this is the
lot P of each giving me this is the
lot P of each giving me this is the posterior probability that hypothesis is
posterior probability that hypothesis is
posterior probability that hypothesis is true so we’re doing science this is how
true so we’re doing science this is how
true so we’re doing science this is how population in our hypothesis after we’ve
population in our hypothesis after we’ve
population in our hypothesis after we’ve done our study so it turns out that we
done our study so it turns out that we
done our study so it turns out that we can use any hypothesis here even though
can use any hypothesis here even though
can use any hypothesis here even though methods so that’s a frequentist
methods so that’s a frequentist
methods so that’s a frequentist statistics that’s except they only use
statistics that’s except they only use
statistics that’s except they only use one term on the left
one term on the left
one term on the left P D given H naught Q H given you know
P D given H naught Q H given you know
P D given H naught Q H given you know these are not the same thing the
these are not the same thing the
these are not the same thing the probability that I’m wearing a shirt
probability that I’m wearing a shirt
probability that I’m wearing a shirt human wearing a red shirt is 100% the
human wearing a red shirt is 100% the
human wearing a red shirt is 100% the probability that wearing a red shirt
probability that wearing a red shirt
probability that wearing a red shirt given that I’m Granger is not understand
given that I’m Granger is not understand
given that I’m Granger is not understand in fact seem to be quite so this is the
in fact seem to be quite so this is the
in fact seem to be quite so this is the main problem in frequency it’s measuring
main problem in frequency it’s measuring
main problem in frequency it’s measuring the wrong thing instead of everything
the wrong thing instead of everything
the wrong thing instead of everything of our hypothesis given our evidence
of our hypothesis given our evidence
of our hypothesis given our evidence unless we probability of getting our
unless we probability of getting our
unless we probability of getting our evidence giving some hypothesis this is
evidence giving some hypothesis this is
evidence giving some hypothesis this is just totally backwards so what’s my
just totally backwards so what’s my
just totally backwards so what’s my third example plus my mate at 4.30 much
third example plus my mate at 4.30 much
third example plus my mate at 4.30 much thinner and one of them is weighted so
thinner and one of them is weighted so
thinner and one of them is weighted so lent heads 80% of the time so we’ll take
lent heads 80% of the time so we’ll take
lent heads 80% of the time so we’ll take the point of random and then try to
the point of random and then try to
the point of random and then try to determine using evidence of pointless
determine using evidence of pointless
determine using evidence of pointless whether the point be pissed
whether the point be pissed
whether the point be pissed well what are the fingertips is those
well what are the fingertips is those
well what are the fingertips is those waiting for the point and it’s heads so
well let’s try our frequences method
well let’s try our frequences method first our hypothesis we put the twenties
first our hypothesis we put the twenties
first our hypothesis we put the twenties week which means of the null hypothesis
week which means of the null hypothesis
week which means of the null hypothesis is fair so what’s the probability given
is fair so what’s the probability given
is fair so what’s the probability given our know that hast given the coin is
our know that hast given the coin is
our know that hast given the coin is fair then we would get the day we got
fair then we would get the day we got
fair then we would get the day we got that we get as well insensitive
that we get as well insensitive
that we get as well insensitive percent of the time so now use the
percent of the time so now use the
percent of the time so now use the standard scientific significance
standard scientific significance
standard scientific significance P is less than point zero five then this
P is less than point zero five then this
P is less than point zero five then this data is not statistically significant
data is not statistically significant
data is not statistically significant which means you can’t learn anything
which means you can’t learn anything
which means you can’t learn anything from the frigid detector with a Bayesian
from the frigid detector with a Bayesian
from the frigid detector with a Bayesian method can make a conclusion let’s use
method can make a conclusion let’s use
method can make a conclusion let’s use Bayes theorem the traffic database so
Bayes theorem the traffic database so
Bayes theorem the traffic database so again we will do each tournament of time
again we will do each tournament of time
again we will do each tournament of time so first we community this is the
so first we community this is the
so first we community this is the probability doing our hypothesis which
probability doing our hypothesis which
probability doing our hypothesis which is that the point is waiting that we
is that the point is waiting that we
is that the point is waiting that we would get the heads so in the way home
would get the heads so in the way home
would get the heads so in the way home Len has a you for some time so this is
Len has a you for some time so this is
Len has a you for some time so this is just 80 percent of point
just 80 percent of point
just 80 percent of point next is P of each this is the prior
next is P of each this is the prior
next is P of each this is the prior probability that would have to be the
probability that would have to be the
probability that would have to be the clock since there’s four points and one
clock since there’s four points and one
clock since there’s four points and one of them is wait at this is just one last
of them is wait at this is just one last
of them is wait at this is just one last piece this is the probability of getting
piece this is the probability of getting
piece this is the probability of getting heads before we do so there’s a poor
heads before we do so there’s a poor
heads before we do so there’s a poor chance we take away that let has 80
chance we take away that let has 80
chance we take away that let has 80 percent there’s a three quarter chance
percent there’s a three quarter chance
percent there’s a three quarter chance when we get one with Fairfax in each of
when we get one with Fairfax in each of
when we get one with Fairfax in each of those one that’s 50 percent of time so
those one that’s 50 percent of time so
those one that’s 50 percent of time so we take all these numbers put them back
we take all these numbers put them back
we take all these numbers put them back to the Bayes theorem to do the matter
to the Bayes theorem to do the matter
to the Bayes theorem to do the matter turns out there’s a 35% chance
turns out there’s a 35% chance
turns out there’s a 35% chance so the point we just split is the way to
so the point we just split is the way to
so the point we just split is the way to talk okay
you’re learning with amazing that the
you’re learning with amazing that the country we were implement interest we
country we were implement interest we
country we were implement interest we changed our probability weighted in 25%
changed our probability weighted in 25%
changed our probability weighted in 25% 35% this is the main advantage of
35% this is the main advantage of
35% this is the main advantage of asia-pacific
asia-pacific
asia-pacific he frequented statistics all infants are
he frequented statistics all infants are
he frequented statistics all infants are black and white either a result a
black and white either a result a
black and white either a result a significant report isn’t all things to
significant report isn’t all things to
significant report isn’t all things to do after they put appointments he said
do after they put appointments he said
do after they put appointments he said that they need to collect more did on
that they need to collect more did on
that they need to collect more did on the other hand of Bayesian to make
the other hand of Bayesian to make
the other hand of Bayesian to make inferences in shades of gray
inferences in shades of gray
inferences in shades of gray Bayesian can also go collect our data to
Bayesian can also go collect our data to
Bayesian can also go collect our data to be more sure but right now they have to
be more sure but right now they have to
be more sure but right now they have to the question how likely is it that the
the question how likely is it that the
the question how likely is it that the point you just love is a later point so
point you just love is a later point so
point you just love is a later point so it turns out the figuring out the prior
it turns out the figuring out the prior
it turns out the figuring out the prior probability of our hypothesis to be is a
probability of our hypothesis to be is a
probability of our hypothesis to be is a lot harder than it was usually than he
lot harder than it was usually than he
lot harder than it was usually than he was not available so you know doing
was not available so you know doing
was not available so you know doing science the prior probability is usually
science the prior probability is usually
science the prior probability is usually subjected to tension adjustment for
subjected to tension adjustment for
subjected to tension adjustment for researchers rather distributed imagine
researchers rather distributed imagine
researchers rather distributed imagine see this means that different scientists
see this means that different scientists
see this means that different scientists for their packaging for the prior
for their packaging for the prior
for their packaging for the prior probabilities about the same hypothesis
probabilities about the same hypothesis
probabilities about the same hypothesis so this might be bad system if what’s
so this might be bad system if what’s
so this might be bad system if what’s science fair publish were probabilities
science fair publish were probabilities
science fair publish were probabilities and hypotheses be triggered because
and hypotheses be triggered because
and hypotheses be triggered because those who depend on subjective
those who depend on subjective
those who depend on subjective probabilities individual researchers so
probabilities individual researchers so
probabilities individual researchers so depending on who did the study together
depending on who did the study together
depending on who did the study together another experiment themselves our
another experiment themselves our
another experiment themselves our objective increased objective results
objective increased objective results
objective increased objective results that shouldn’t depend on researcher so
that shouldn’t depend on researcher so
that shouldn’t depend on researcher so let’s go back acquaintance except this
let’s go back acquaintance except this
let’s go back acquaintance except this time we don’t know
time we don’t know
time we don’t know and don’t know prior probability we’re
and don’t know prior probability we’re
and don’t know prior probability we’re just sticking point
just sticking point
just sticking point totally Atlanta only knows that it’s
totally Atlanta only knows that it’s
totally Atlanta only knows that it’s either way tonight has made seven times
either way tonight has made seven times
either way tonight has made seven times understand so once we do then we
understand so once we do then we
understand so once we do then we calculate is called with base batteries
calculate is called with base batteries
calculate is called with base batteries the base factor depends only on the
the base factor depends only on the
the base factor depends only on the experimental result degree two
experimental result degree two
experimental result degree two hypotheses say each one h9 the base
hypotheses say each one h9 the base
hypotheses say each one h9 the base factor is P P given h1 so with our phone
factor is P P given h1 so with our phone
factor is P P given h1 so with our phone if each one is not possible to the point
if each one is not possible to the point
if each one is not possible to the point its way to speech not fair then the
its way to speech not fair then the
its way to speech not fair then the probability people get ahead my weight
probability people get ahead my weight
probability people get ahead my weight is 8% eight probably isn’t a spare fifty
is 8% eight probably isn’t a spare fifty
is 8% eight probably isn’t a spare fifty percent of five so base factors just one
percent of five so base factors just one
percent of five so base factors just one point six so what can you do is well we
point six so what can you do is well we
point six so what can you do is well we can multiply our base factor by our
can multiply our base factor by our
can multiply our base factor by our primary nerd calculate where posterior
primary nerd calculate where posterior
primary nerd calculate where posterior won’t we shouldn’t need after we see
won’t we shouldn’t need after we see
won’t we shouldn’t need after we see evidence
evidence
evidence so that’s so Bayes factor that’s what we
so that’s so Bayes factor that’s what we
so that’s so Bayes factor that’s what we actually need a science that’s actually
actually need a science that’s actually
actually need a science that’s actually record so instead trying to make
record so instead trying to make
record so instead trying to make inferences or get results that the
inferences or get results that the
inferences or get results that the participates
participates
participates we want to resolve to change our beliefs
we want to resolve to change our beliefs
we want to resolve to change our beliefs in the direction of truth this was the
in the direction of truth this was the
in the direction of truth this was the base factor of the students so science
base factor of the students so science
base factor of the students so science we need to know what about your data
we need to know what about your data
we need to know what about your data before campaigns at all there are times
before campaigns at all there are times
before campaigns at all there are times harder where we ended up on diesel take
harder where we ended up on diesel take
harder where we ended up on diesel take the following problem
the following problem
the following problem 10 out of every thousand residents of
10 out of every thousand residents of
10 out of every thousand residents of these tournament with breast cancer 90
these tournament with breast cancer 90
these tournament with breast cancer 90 excess power of the remaining 990 women
excess power of the remaining 990 women
excess power of the remaining 990 women 89 will also test object a woman test
89 will also test object a woman test
89 will also test object a woman test possible he wants to know whether she’s
possible he wants to know whether she’s
possible he wants to know whether she’s breast cancer for sure or these from the
breast cancer for sure or these from the
breast cancer for sure or these from the tentacles
tentacles
tentacles so this problem presented over $4,000
so this problem presented over $4,000
so this problem presented over $4,000 well this is actually not too far solved
well this is actually not too far solved
well this is actually not too far solved using Bayes theorem and really Seoul say
using Bayes theorem and really Seoul say
using Bayes theorem and really Seoul say the hypothesis it’s a woman’s breast
the hypothesis it’s a woman’s breast
the hypothesis it’s a woman’s breast cancer any evidence that she tested
cancer any evidence that she tested
cancer any evidence that she tested positive so we can just use Bayes
positive so we can just use Bayes
positive so we can just use Bayes thinner as we do I want the gross stuff
thinner as we do I want the gross stuff
thinner as we do I want the gross stuff this time
this time
this time turns out there’s about a 90% chance
turns out there’s about a 90% chance
turns out there’s about a 90% chance here’s the problem a 1/4 of doctors
here’s the problem a 1/4 of doctors
here’s the problem a 1/4 of doctors answer this question correct this is a
answer this question correct this is a
answer this question correct this is a multiple choice question by problem
multiple choice question by problem
multiple choice question by problem another full quarter doctors so that the
another full quarter doctors so that the
another full quarter doctors so that the woman has
woman has
woman has that’s a huge thing it makes total sense
that’s a huge thing it makes total sense
that’s a huge thing it makes total sense these doctors who made this mistake
these doctors who made this mistake
these doctors who made this mistake because they all their lives so is the
because they all their lives so is the
because they all their lives so is the probability both the show
probability both the show
probability both the show but also we can’t always go for dinner
but also we can’t always go for dinner
but also we can’t always go for dinner each women only got past what so it
each women only got past what so it
each women only got past what so it seems similar bandages to use amazing
seems similar bandages to use amazing
seems similar bandages to use amazing statistics rather than the standard of
statistics rather than the standard of
statistics rather than the standard of treatment in statistics use bioscience
treatment in statistics use bioscience
treatment in statistics use bioscience if sons you may sit to visit people will
if sons you may sit to visit people will
if sons you may sit to visit people will be better
be better
be better Beasley’s and they do better the proper
Beasley’s and they do better the proper
Beasley’s and they do better the proper kind of problem that we just saw science
kind of problem that we just saw science
kind of problem that we just saw science based on all the information disposal
based on all the information disposal
based on all the information disposal not just
not just
not just mythix really make conclusions that work
mythix really make conclusions that work
mythix really make conclusions that work just black and white with the country’s
just black and white with the country’s
just black and white with the country’s brightest and most normal be able to
brightest and most normal be able to
brightest and most normal be able to have scientifically informed beliefs
have scientifically informed beliefs
have scientifically informed beliefs about how these beliefs about the way
about how these beliefs about the way
about how these beliefs about the way the one who actually did
”
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