TEDNYC
Mona Chalabi: 3 ways to spot a bad statistic
Mona Čalabi (Mona Chalabi): Tri načina da prepoznate loše statističke podatke
Filmed:
Readability: 4.2
1,888,599 views
Ponekad je teško znati koji statistički podaci su vredni poverenja, ali ih ne treba potpuno otpisati. Umesto toga, trebalo bi da naučimo da sagledamo stvari iza njih. U ovom divnom, urnebesnom govoru, novinarka koja se bavi podacima Mona Čalabi deli korisne savete da bi pomogla u preispitivanju, tumačenju i stvarnom razumevanju onoga što brojevi govore.
Mona Chalabi - Data journalist
Mona Chalabi tries to take the numb out of numbers. She's left with lots of "ers." Full bio
Mona Chalabi tries to take the numb out of numbers. She's left with lots of "ers." Full bio
Double-click the English transcript below to play the video.
Danas ću govoriti o statistici.
00:12
Now, I'm going to be talking
about statistics today.
about statistics today.
0
884
2763
Ako se zbog toga odmah osećate
pomalo obazrivo, to je u redu;
pomalo obazrivo, to je u redu;
00:15
If that makes you immediately feel
a little bit wary, that's OK,
a little bit wary, that's OK,
1
3671
3138
to vas ne čini ludim teoretičarem zavere,
00:18
that doesn't make you some
kind of crazy conspiracy theorist,
kind of crazy conspiracy theorist,
2
6833
2859
00:21
it makes you skeptical.
3
9716
1296
već vas čini skeptičnim.
00:23
And when it comes to numbers,
especially now, you should be skeptical.
especially now, you should be skeptical.
4
11036
3886
A kada se radi o brojevima, pogotovo sada,
treba da budete skeptični.
treba da budete skeptični.
Ali, takođe bi trebalo
da možete da prepoznate
da možete da prepoznate
00:26
But you should also be able to tell
which numbers are reliable
which numbers are reliable
5
14946
3011
koji brojevi su pouzdani, a koji nisu.
00:29
and which ones aren't.
6
17981
1160
Danas želim da probam da vam pružim
izvesna pomagala da biste to umeli.
izvesna pomagala da biste to umeli.
00:31
So today I want to try to give you
some tools to be able to do that.
some tools to be able to do that.
7
19165
3206
00:34
But before I do,
8
22395
1169
Pre nego što to uradim,
00:35
I just want to clarify which numbers
I'm talking about here.
I'm talking about here.
9
23588
2839
želim samo da razjasnim
o kojim brojevima ovde govorim.
o kojim brojevima ovde govorim.
Ne govorim o tvrdnjama poput:
00:38
I'm not talking about claims like,
10
26451
1635
„Devet od deset žena preporučuje
ovu kremu protiv bora.“
ovu kremu protiv bora.“
00:40
"9 out of 10 women recommend
this anti-aging cream."
this anti-aging cream."
11
28110
2449
Mislim da veliki broj nas
uvek prevrće očima na takve brojeve.
uvek prevrće očima na takve brojeve.
00:42
I think a lot of us always
roll our eyes at numbers like that.
roll our eyes at numbers like that.
12
30583
2972
Ono što je sada drugačije je što ljudi
dovode u pitanje podatke poput:
dovode u pitanje podatke poput:
00:45
What's different now is people
are questioning statistics like,
are questioning statistics like,
13
33579
2984
„Nezaposlenost u SAD je pet procenata.“
00:48
"The US unemployment
rate is five percent."
rate is five percent."
14
36587
2014
Ono po čemu je ova tvrdnja drugačija
je to što ne proističe iz privatne firme,
je to što ne proističe iz privatne firme,
00:50
What makes this claim different is
it doesn't come from a private company,
it doesn't come from a private company,
15
38625
3516
već iz vlade.
00:54
it comes from the government.
16
42165
1388
Oko četiri od deset Amerikanaca
ne veruje ekonomskim podacima
ne veruje ekonomskim podacima
00:55
About 4 out of 10 Americans
distrust the economic data
distrust the economic data
17
43577
3336
00:58
that gets reported by government.
18
46937
1573
o kojima izveštava vlast.
Među pristalicama predsednika Trampa,
taj broj je još veći; oko sedam od deset.
taj broj je još veći; oko sedam od deset.
01:00
Among supporters of President Trump
it's even higher;
it's even higher;
19
48534
2491
01:03
it's about 7 out of 10.
20
51049
1633
01:04
I don't need to tell anyone here
21
52706
1804
Ne treba nikome ovde da pričam
01:06
that there are a lot of dividing lines
in our society right now,
in our society right now,
22
54534
3011
da u našem društvu danas postoji
mnogo linija razdvajanja,
mnogo linija razdvajanja,
01:09
and a lot of them start to make sense,
23
57569
1825
a mnoge počinju da imaju smisla
kada razumete odnos ljudi
prema tim vladinim brojevima.
prema tim vladinim brojevima.
01:11
once you understand people's relationships
with these government numbers.
with these government numbers.
24
59418
3687
Sa jedne strane, tu su oni koji kažu
da su ovi podaci od ključnog značaja,
da su ovi podaci od ključnog značaja,
01:15
On the one hand, there are those who say
these statistics are crucial,
these statistics are crucial,
25
63129
3336
da su nam potrebni da bi nam društvo
kao celina imalo smisla,
kao celina imalo smisla,
01:18
that we need them to make sense
of society as a whole
of society as a whole
26
66489
2630
01:21
in order to move beyond
emotional anecdotes
emotional anecdotes
27
69143
2164
kako bismo prevazišli emocionale anegdote
01:23
and measure progress in a subjective way.
28
71331
2410
i merili napredak na objektivan način.
Zatim, tu su oni drugi,
01:25
And then there are the others,
29
73765
1467
koji kažu da su ovi podaci elitistički,
01:27
who say that these statistics are elitist,
30
75256
2156
možda čak i namešteni;
01:29
maybe even rigged;
31
77436
1208
01:30
they don't make sense
and they don't really reflect
and they don't really reflect
32
78668
2394
da nemaju smisla i ne odražavaju zaista
ono što se dešava
u svakodnevnom životu ljudi.
u svakodnevnom životu ljudi.
01:33
what's happening
in people's everyday lives.
in people's everyday lives.
33
81086
2296
01:35
It kind of feels like that second group
is winning the argument right now.
is winning the argument right now.
34
83406
3487
Nekako deluje da druga grupa
trenutno pobeđuje u raspravi.
trenutno pobeđuje u raspravi.
Živimo u svetu alternativnih činjenica,
01:38
We're living in a world
of alternative facts,
of alternative facts,
35
86917
2108
gde ljudi ne smatraju statističke podatke
nekom vrstom zajedničke osnove,
nekom vrstom zajedničke osnove,
01:41
where people don't find statistics
this kind of common ground,
this kind of common ground,
36
89049
2935
01:44
this starting point for debate.
37
92008
1636
početnom tačkom za debatu.
To je problem.
01:45
This is a problem.
38
93668
1286
Trenutno zapravo postoje pokreti u SAD
01:46
There are actually
moves in the US right now
moves in the US right now
39
94978
2067
da se potpuno otarasimo
nekih vladinih statističkih podataka.
nekih vladinih statističkih podataka.
01:49
to get rid of some government
statistics altogether.
statistics altogether.
40
97069
2861
Baš sada postoji predlog zakona u kongresu
o merenju rasne nejednakosti.
o merenju rasne nejednakosti.
01:51
Right now there's a bill in congress
about measuring racial inequality.
about measuring racial inequality.
41
99954
3387
Nacrt zakona kaže da novac vlade
ne treba koristiti
ne treba koristiti
01:55
The draft law says that government
money should not be used
money should not be used
42
103365
2801
za prikupljanje podataka
o rasnoj segregaciji.
o rasnoj segregaciji.
01:58
to collect data on racial segregation.
43
106190
1902
To je potpuna katastrofa.
02:00
This is a total disaster.
44
108116
1885
Ako nemamo ove podatke,
02:02
If we don't have this data,
45
110025
1748
kako možemo da posmatramo diskriminaciju,
02:03
how can we observe discrimination,
46
111797
1778
02:05
let alone fix it?
47
113599
1278
a kamoli da je popravimo?
Drugim rečima,
02:06
In other words:
48
114901
1188
kako vlast može da stvara
pravednu politiku
pravednu politiku
02:08
How can a government create fair policies
49
116113
2059
ako ne može da izmeri
trenutni nivo nepravednosti?
trenutni nivo nepravednosti?
02:10
if they can't measure
current levels of unfairness?
current levels of unfairness?
50
118196
2771
Ovde se ne radi samo o diskriminaciji,
već o svemu; razmislite o tome.
već o svemu; razmislite o tome.
02:12
This isn't just about discrimination,
51
120991
1794
02:14
it's everything -- think about it.
52
122809
1670
Kako možemo donositi zakone
o zdravstvenoj zaštiti
o zdravstvenoj zaštiti
02:16
How can we legislate on health care
53
124503
1690
02:18
if we don't have good data
on health or poverty?
on health or poverty?
54
126217
2271
ako nemamo dobre podatke
o zdravlju ili siromaštvu?
o zdravlju ili siromaštvu?
02:20
How can we have public debate
about immigration
about immigration
55
128512
2198
Kako možemo javno debatovati o imigraciji
ako se ne možemo makar složiti
ako se ne možemo makar složiti
02:22
if we can't at least agree
56
130734
1250
02:24
on how many people are entering
and leaving the country?
and leaving the country?
57
132008
2643
oko toga koliko ljudi
ulazi u zemlju i izlazi iz nje?
ulazi u zemlju i izlazi iz nje?
Statistički podaci proističu iz države;
tako su dobili svoje ime.
tako su dobili svoje ime.
02:26
Statistics come from the state;
that's where they got their name.
that's where they got their name.
58
134675
3058
Poenta je bila da se dobiju
bolje mere stanovništva
bolje mere stanovništva
02:29
The point was to better
measure the population
measure the population
59
137757
2157
02:31
in order to better serve it.
60
139938
1357
kako bi mu se bolje služilo.
02:33
So we need these government numbers,
61
141319
1725
Dakle, potrebni su nam ti vladini brojevi,
02:35
but we also have to move
beyond either blindly accepting
beyond either blindly accepting
62
143068
2647
ali treba i da prevaziđemo
njihovo slepo prihvatanje,
njihovo slepo prihvatanje,
02:37
or blindly rejecting them.
63
145739
1268
kao i slepo odbacivanje.
Potrebne su nam veštine
da bismo mogli da uočimo loše podatke.
da bismo mogli da uočimo loše podatke.
02:39
We need to learn the skills
to be able to spot bad statistics.
to be able to spot bad statistics.
64
147031
2997
02:42
I started to learn some of these
65
150052
1528
Počela sam da ih stičem
kada sam radila na odeljenju za statistiku
02:43
when I was working
in a statistical department
in a statistical department
66
151604
2166
koje je deo Ujedinjenih nacija.
02:45
that's part of the United Nations.
67
153794
1643
Naš posao je bio da saznamo koliko Iračana
je proterano iz svojih domova
je proterano iz svojih domova
02:47
Our job was to find out how many Iraqis
had been forced from their homes
had been forced from their homes
68
155461
3406
kao posledica rata
02:50
as a result of the war,
69
158891
1158
i šta im je potrebno.
02:52
and what they needed.
70
160073
1158
To je bio zaista važan posao,
ali, takođe, neverovatno težak.
ali, takođe, neverovatno težak.
02:53
It was really important work,
but it was also incredibly difficult.
but it was also incredibly difficult.
71
161255
3178
Svakoga dana smo donosili odluke
02:56
Every single day, we were making decisions
72
164457
2018
koje su uticale na tačnost naših brojeva -
02:58
that affected the accuracy
of our numbers --
of our numbers --
73
166499
2157
odluke poput toga u koje delove zemlje
bi trebalo da idemo,
bi trebalo da idemo,
03:00
decisions like which parts
of the country we should go to,
of the country we should go to,
74
168680
2744
sa kim treba da razgovaramo,
koja pitanja treba da postavljamo.
koja pitanja treba da postavljamo.
03:03
who we should speak to,
75
171448
1156
03:04
which questions we should ask.
76
172628
1568
03:06
And I started to feel
really disillusioned with our work,
really disillusioned with our work,
77
174220
2680
Počela sam da se osećam zaista razočarano
u vezi sa našim radom,
u vezi sa našim radom,
03:08
because we thought we were doing
a really good job,
a really good job,
78
176924
2518
jer smo mislili
da zaista dobro obavljamo posao,
da zaista dobro obavljamo posao,
03:11
but the one group of people
who could really tell us were the Iraqis,
who could really tell us were the Iraqis,
79
179466
3278
ali ona grupa ljudi koja bi stvarno mogla
da nam ispriča stvari bili su Iračani,
da nam ispriča stvari bili su Iračani,
03:14
and they rarely got the chance to find
our analysis, let alone question it.
our analysis, let alone question it.
80
182768
3540
a oni su retko imali priliku
da naiđu na našu analizu,
da naiđu na našu analizu,
a kamoli da je preispituju.
03:18
So I started to feel really determined
81
186332
1831
Zato sam postala zaista odlučna
03:20
that the one way to make
numbers more accurate
numbers more accurate
82
188187
2311
da je jedini način da postignemo
da brojevi budu tačniji
da brojevi budu tačniji
03:22
is to have as many people as possible
be able to question them.
be able to question them.
83
190522
3053
da omogućimo da što više ljudi
može da ih preispituje.
može da ih preispituje.
03:25
So I became a data journalist.
84
193599
1434
Tako sam postala novinarka
koja se bavi podacima.
koja se bavi podacima.
03:27
My job is finding these data sets
and sharing them with the public.
and sharing them with the public.
85
195057
3904
Moj posao je da pronađem skupove podataka
i podelim ih sa javnošću.
i podelim ih sa javnošću.
03:30
Anyone can do this,
you don't have to be a geek or a nerd.
you don't have to be a geek or a nerd.
86
198985
3173
Bilo ko to može, ne morate biti
štreber ili bubalica.
štreber ili bubalica.
Možete zanemariti te reči;
koriste ih ljudi
koriste ih ljudi
03:34
You can ignore those words;
they're used by people
they're used by people
87
202182
2355
koji pokušavaju da kažu da su pametni
dok se pretvaraju da su skromni.
dok se pretvaraju da su skromni.
03:36
trying to say they're smart
while pretending they're humble.
while pretending they're humble.
88
204561
2822
Apsolutno svako to može.
03:39
Absolutely anyone can do this.
89
207407
1589
Htela bih da vam postavim tri pitanja
03:41
I want to give you guys three questions
90
209020
2067
koja će vam pomoći da možete
da primetite loše statističke podatke.
da primetite loše statističke podatke.
03:43
that will help you be able to spot
some bad statistics.
some bad statistics.
91
211111
3005
Dakle, pitanje broj jedan glasi:
možete li da uočite nepouzdanost?
možete li da uočite nepouzdanost?
03:46
So, question number one
is: Can you see uncertainty?
is: Can you see uncertainty?
92
214140
3507
Jedna od stvari koja je zaista promenila
odnos ljudi prema brojevima,
odnos ljudi prema brojevima,
03:49
One of things that's really changed
people's relationship with numbers,
people's relationship with numbers,
93
217671
3364
pa čak i njihovo poverenje u medije,
03:53
and even their trust in the media,
94
221059
1641
bilo je korišćenje političkih anketa.
03:54
has been the use of political polls.
95
222724
2258
Lično imam mnogo problema
sa političkim anketama
sa političkim anketama
03:57
I personally have a lot of issues
with political polls
with political polls
96
225006
2538
jer smatram da je uloga novinara
da izveštava o činjenicama,
da izveštava o činjenicama,
03:59
because I think the role of journalists
is actually to report the facts
is actually to report the facts
97
227568
3376
a ne da pokušava da ih predvidi,
04:02
and not attempt to predict them,
98
230968
1553
naročito kada ta previđanja
mogu da naškode demokratiji
mogu da naškode demokratiji
04:04
especially when those predictions
can actually damage democracy
can actually damage democracy
99
232545
2996
davanjem signala ljudima
da se ne trude da glasaju za nekog
da se ne trude da glasaju za nekog
04:07
by signaling to people:
don't bother to vote for that guy,
don't bother to vote for that guy,
100
235565
2732
jer nema šanse.
04:10
he doesn't have a chance.
101
238321
1205
Stavimo to po strani za sada i popričajmo
o preciznosti ovog nastojanja.
o preciznosti ovog nastojanja.
04:11
Let's set that aside for now and talk
about the accuracy of this endeavor.
about the accuracy of this endeavor.
102
239550
3654
Na osnovu državnih izbora
04:15
Based on national elections
in the UK, Italy, Israel
in the UK, Italy, Israel
103
243228
4608
u Ujedinjenom Kraljevstvu,
Italiji, Izraelu
Italiji, Izraelu
04:19
and of course, the most recent
US presidential election,
US presidential election,
104
247860
2764
i, naravno, najskorijih
predsedničkih izbora u SAD,
predsedničkih izbora u SAD,
korišćenje anketa
za predviđanje ishoda izbora
za predviđanje ishoda izbora
04:22
using polls to predict electoral outcomes
105
250648
2137
otprilike je tačno kao korišćenje meseca
za predviđanje prijema u bolnice.
za predviđanje prijema u bolnice.
04:24
is about as accurate as using the moon
to predict hospital admissions.
to predict hospital admissions.
106
252809
3812
Ne, ozbiljno, koristila sam
stvarne podatke iz akademske studije
stvarne podatke iz akademske studije
04:28
No, seriously, I used actual data
from an academic study to draw this.
from an academic study to draw this.
107
256645
4200
da bih ovo nacrtala.
04:32
There are a lot of reasons why
polling has become so inaccurate.
polling has become so inaccurate.
108
260869
3727
Postoji mnogo razloga
zašto je anketiranje postalo tako netačno.
zašto je anketiranje postalo tako netačno.
Naša društva su postala veoma raznolika,
04:36
Our societies have become really diverse,
109
264620
1970
što otežava anketarima da dobiju
fini reprezentativni uzorak stanovništva
fini reprezentativni uzorak stanovništva
04:38
which makes it difficult for pollsters
to get a really nice representative sample
to get a really nice representative sample
110
266614
3821
04:42
of the population for their polls.
111
270459
1627
za svoje ankete.
Ljudi se nerado javljaju
na telefon anketarima,
na telefon anketarima,
04:44
People are really reluctant to answer
their phones to pollsters,
their phones to pollsters,
112
272110
3006
a, takođe, ko bi rekao,
ljudi mogu da slažu.
ljudi mogu da slažu.
04:47
and also, shockingly enough,
people might lie.
people might lie.
113
275140
2276
04:49
But you wouldn't necessarily
know that to look at the media.
know that to look at the media.
114
277440
2811
Međutim, to nećete nužno znati
pogledavši medije.
pogledavši medije.
04:52
For one thing, the probability
of a Hillary Clinton win
of a Hillary Clinton win
115
280275
2761
Između ostalog, o verovatnoći
pobede Hilari Klinton
pobede Hilari Klinton
04:55
was communicated with decimal places.
116
283060
2791
izveštavano je pomoću decimalnih brojeva.
Ne koristimo decimale
da izrazimo temperaturu.
da izrazimo temperaturu.
04:57
We don't use decimal places
to describe the temperature.
to describe the temperature.
117
285875
2621
Kako, pobogu, predviđanje ponašanja
230 miliona glasača u ovoj zemlji
230 miliona glasača u ovoj zemlji
05:00
How on earth can predicting the behavior
of 230 million voters in this country
of 230 million voters in this country
118
288520
4228
može biti tako precizno?
05:04
be that precise?
119
292772
1829
05:06
And then there were those sleek charts.
120
294625
2002
Zatim, tu su bili oni doterani grafikoni.
05:08
See, a lot of data visualizations
will overstate certainty, and it works --
will overstate certainty, and it works --
121
296651
3973
Vidite, mnogo vizualizacije podataka
će prenaglasiti sigurnost, i to deluje -
će prenaglasiti sigurnost, i to deluje -
05:12
these charts can numb
our brains to criticism.
our brains to criticism.
122
300648
2620
ovi grafikoni mogu da otupe
naš mozak za kriticizam.
naš mozak za kriticizam.
05:15
When you hear a statistic,
you might feel skeptical.
you might feel skeptical.
123
303292
2558
Kada čujete podatak,
možda ćete biti skeptični.
možda ćete biti skeptični.
05:17
As soon as it's buried in a chart,
124
305874
1635
Čim je upakovan u grafikon,
čini se kao nekakva objektivna nauka,
05:19
it feels like some kind
of objective science,
of objective science,
125
307533
2129
a nije.
05:21
and it's not.
126
309686
1249
Zato sam pokušavala da pronađem načine
da ovo bolje prenesem ljudima,
da ovo bolje prenesem ljudima,
05:22
So I was trying to find ways
to better communicate this to people,
to better communicate this to people,
127
310959
3103
da im pokažem nesigurnost
u našim brojevima.
u našim brojevima.
05:26
to show people the uncertainty
in our numbers.
in our numbers.
128
314086
2504
05:28
What I did was I started taking
real data sets,
real data sets,
129
316614
2246
Počela sam da uzimam
stvarne skupove podataka
stvarne skupove podataka
05:30
and turning them into
hand-drawn visualizations,
hand-drawn visualizations,
130
318884
2652
i pretvaram ih u vizualizacije
nacrtane rukom,
nacrtane rukom,
tako da ljudi mogu da vide
koliko su podaci neprecizni;
koliko su podaci neprecizni;
05:33
so that people can see
how imprecise the data is;
how imprecise the data is;
131
321560
2672
tako da mogu da vide
da je to uradilo ljudsko biće,
da je to uradilo ljudsko biće,
05:36
so people can see that a human did this,
132
324256
1996
05:38
a human found the data and visualized it.
133
326276
1972
čovek je našao podatke i vizualizovao ih.
05:40
For example, instead
of finding out the probability
of finding out the probability
134
328272
2672
Na primer, umesto saznavanja verovatnoće
05:42
of getting the flu in any given month,
135
330968
2126
da dobijete grip u bilo kom mesecu,
05:45
you can see the rough
distribution of flu season.
distribution of flu season.
136
333118
2792
možete videti grubu raspodelu
sezone gripa.
sezone gripa.
05:47
This is --
137
335934
1167
Ovo je -
(Smeh)
05:49
(Laughter)
138
337125
1018
loš grafikon za pokazivanje u februaru.
05:50
a bad shot to show in February.
139
338167
1486
Međutim, takođe je odgovornija
vizualizacija podataka,
vizualizacija podataka,
05:51
But it's also more responsible
data visualization,
data visualization,
140
339677
2455
05:54
because if you were to show
the exact probabilities,
the exact probabilities,
141
342156
2455
jer ako biste pokazali tačne verovatnoće,
možda bi to podstaklo ljude
da dobiju vakcine protiv gripa
da dobiju vakcine protiv gripa
05:56
maybe that would encourage
people to get their flu jabs
people to get their flu jabs
142
344635
2592
u pogrešno vreme.
05:59
at the wrong time.
143
347251
1456
06:01
The point of these shaky lines
144
349163
1693
Svrha ovih nesigurnih linija
je da ljudi upamte te nepreciznosti,
06:02
is so that people remember
these imprecisions,
these imprecisions,
145
350880
2911
ali i da ne ponesu
nužno sa sobom određeni broj,
nužno sa sobom određeni broj,
06:05
but also so they don't necessarily
walk away with a specific number,
walk away with a specific number,
146
353815
3227
već da mogu zapamtiti važne činjenice.
06:09
but they can remember important facts.
147
357066
1866
Činjenice poput toga
da nepravde i nejednakosti
da nepravde i nejednakosti
06:10
Facts like injustice and inequality
leave a huge mark on our lives.
leave a huge mark on our lives.
148
358956
4024
ostavljaju veliki trag u našem životu.
06:15
Facts like Black Americans and Native
Americans have shorter life expectancies
Americans have shorter life expectancies
149
363004
4189
Činjenice poput toga da američki crnci
i Indijanci imaju kraći životni vek
i Indijanci imaju kraći životni vek
od pripadnika drugih rasa,
06:19
than those of other races,
150
367217
1400
a to se neće u skorije vreme promeniti.
06:20
and that isn't changing anytime soon.
151
368641
2138
06:22
Facts like prisoners in the US
can be kept in solitary confinement cells
can be kept in solitary confinement cells
152
370803
3901
Činjenice poput toga da se zatvorenici
u SAD mogu držati u samicama
u SAD mogu držati u samicama
06:26
that are smaller than the size
of an average parking space.
of an average parking space.
153
374728
3342
koje su manje od veličine
prosečnog mesta za parkiranje.
prosečnog mesta za parkiranje.
06:30
The point of these visualizations
is also to remind people
is also to remind people
154
378535
3335
Svrha ovih vizualizacija
takođe je da se ljudi podsete
takođe je da se ljudi podsete
06:33
of some really important
statistical concepts,
statistical concepts,
155
381894
2350
nekih veoma važnih statističkih koncepata,
06:36
concepts like averages.
156
384268
1636
kao što su prosečne vrednosti.
Recimo da čujete tvrdnju kao što je:
06:37
So let's say you hear a claim like,
157
385928
1668
„Prosečan bazen u SAD
sadrži 6,23 fekalnih nezgoda.“
sadrži 6,23 fekalnih nezgoda.“
06:39
"The average swimming pool in the US
contains 6.23 fecal accidents."
contains 6.23 fecal accidents."
158
387620
4434
To ne znači da svaki bazen u zemlji
06:44
That doesn't mean every single
swimming pool in the country
swimming pool in the country
159
392078
2797
sadrži tačno 6,23 komada izmeta.
06:46
contains exactly 6.23 turds.
160
394899
2194
Kako bih to pokazala,
06:49
So in order to show that,
161
397117
1417
okrenula sam se prvobitnim podacima
iz Centra za kontrolu i prevenciju bolesti
iz Centra za kontrolu i prevenciju bolesti
06:50
I went back to the original data,
which comes from the CDC,
which comes from the CDC,
162
398558
2841
06:53
who surveyed 47 swimming facilities.
163
401423
2065
koji je izvršio procenu
47 objekata za plivanje.
47 objekata za plivanje.
06:55
And I just spent one evening
redistributing poop.
redistributing poop.
164
403512
2391
A ja sam samo provela jedno veče
u preraspodeli kake.
u preraspodeli kake.
06:57
So you can kind of see
how misleading averages can be.
how misleading averages can be.
165
405927
2682
Tako da možete videti
kako prosek može da obmane.
kako prosek može da obmane.
07:00
(Laughter)
166
408633
1282
(Smeh)
07:01
OK, so the second question
that you guys should be asking yourselves
that you guys should be asking yourselves
167
409939
3901
U redu, drugo pitanje
koje treba da postavite sebi
koje treba da postavite sebi
da biste uočili loše brojeve
07:05
to spot bad numbers is:
168
413864
1501
je da li vidite sebe u podacima.
07:07
Can I see myself in the data?
169
415389
1967
Ovo pitanje se na neki način
odnosi i na prosečne vrednosti,
odnosi i na prosečne vrednosti,
07:09
This question is also
about averages in a way,
about averages in a way,
170
417380
2913
jer je deo razloga
zašto ljude toliko frustriraju
zašto ljude toliko frustriraju
07:12
because part of the reason
why people are so frustrated
why people are so frustrated
171
420317
2605
ovi nacionalni statistički podaci
07:14
with these national statistics,
172
422946
1495
to što ne iznose priču
o tome ko pobeđuje a ko je na gubitku
o tome ko pobeđuje a ko je na gubitku
07:16
is they don't really tell the story
of who's winning and who's losing
of who's winning and who's losing
173
424465
3273
usled državne politike.
07:19
from national policy.
174
427762
1156
Lako je razumeti zašto ljude frustriraju
globalne prosečne vrednosti
globalne prosečne vrednosti
07:20
It's easy to understand why people
are frustrated with global averages
are frustrated with global averages
175
428942
3318
kada se ne poklapaju
sa njihovim ličnim iskustvom.
sa njihovim ličnim iskustvom.
07:24
when they don't match up
with their personal experiences.
with their personal experiences.
176
432284
2679
Htela sam da pokažem ljudima
07:26
I wanted to show people the way
data relates to their everyday lives.
data relates to their everyday lives.
177
434987
3263
kako podaci imaju veze
sa njihovim svakodnevnim životom.
sa njihovim svakodnevnim životom.
Pokrenula sam rubriku za savete
pod nazivom „Draga Mona“,
pod nazivom „Draga Mona“,
07:30
I started this advice column
called "Dear Mona,"
called "Dear Mona,"
178
438274
2246
gde bi mi ljudi pisali
i iznosili svoja pitanja i probleme,
i iznosili svoja pitanja i probleme,
07:32
where people would write to me
with questions and concerns
with questions and concerns
179
440544
2726
a ja bih pokušala da im odgovorim
pomoću podataka.
pomoću podataka.
07:35
and I'd try to answer them with data.
180
443294
1784
07:37
People asked me anything.
181
445102
1200
Ljudi su me svašta pitali,
07:38
questions like, "Is it normal to sleep
in a separate bed to my wife?"
in a separate bed to my wife?"
182
446326
3261
na primer: „Da li je normalno da spavam
u odvojenom krevetu od svoje žene?“
u odvojenom krevetu od svoje žene?“
07:41
"Do people regret their tattoos?"
183
449611
1591
„Da li se ljudi kaju
zbog svojih tetovaža?“
zbog svojih tetovaža?“
07:43
"What does it mean to die
of natural causes?"
of natural causes?"
184
451226
2164
„Šta znači umreti prirodnom smrću?“
07:45
All of these questions are great,
because they make you think
because they make you think
185
453414
2966
Sva ta pitanja su sjajna,
jer vas teraju da razmislite
jer vas teraju da razmislite
07:48
about ways to find
and communicate these numbers.
and communicate these numbers.
186
456404
2336
o tome kako da saznate
i saopštite ove brojeve.
i saopštite ove brojeve.
Ako vas neko pita:
„Koliko piškenja je mnogo?“,
„Koliko piškenja je mnogo?“,
07:50
If someone asks you,
"How much pee is a lot of pee?"
"How much pee is a lot of pee?"
187
458764
2503
što je pitanje koje sam ja dobila,
07:53
which is a question that I got asked,
188
461291
2458
07:55
you really want to make sure
that the visualization makes sense
that the visualization makes sense
189
463773
2980
želite da se postarate
da vizualizacija ima smisla
da vizualizacija ima smisla
za što je više ljudi moguće.
07:58
to as many people as possible.
190
466777
1747
08:00
These numbers aren't unavailable.
191
468548
1575
Ovi brojevi nisu nedostupni.
08:02
Sometimes they're just buried
in the appendix of an academic study.
in the appendix of an academic study.
192
470147
3507
Ponekad su samo zakopani
u prilogu akademske studije.
u prilogu akademske studije.
08:05
And they're certainly not inscrutable;
193
473678
1839
A svakako nisu nedokučivi;
ako zaista želite da proverite
ove brojeve o količini mokrenja,
ove brojeve o količini mokrenja,
08:07
if you really wanted to test
these numbers on urination volume,
these numbers on urination volume,
194
475541
2975
možete uzeti bočicu i pokušati sami.
08:10
you could grab a bottle
and try it for yourself.
and try it for yourself.
195
478540
2257
(Smeh)
08:12
(Laughter)
196
480821
1008
Suština ovoga nije nužno
08:13
The point of this isn't necessarily
197
481853
1694
da svaki skup podataka
mora da se izričito odnosi na vas.
mora da se izričito odnosi na vas.
08:15
that every single data set
has to relate specifically to you.
has to relate specifically to you.
198
483571
2877
Mene zanima koliko žena
je dobilo novčanu kaznu u Francuskoj
je dobilo novčanu kaznu u Francuskoj
08:18
I'm interested in how many women
were issued fines in France
were issued fines in France
199
486472
2880
za nošenje vela na licu, ili nikaba,
08:21
for wearing the face veil, or the niqab,
200
489376
1959
čak iako ne živim u Francuskoj
niti nosim veo preko lica.
niti nosim veo preko lica.
08:23
even if I don't live in France
or wear the face veil.
or wear the face veil.
201
491359
2618
Poenta postavljanja pitanja
gde se vi uklapate
gde se vi uklapate
08:26
The point of asking where you fit in
is to get as much context as possible.
is to get as much context as possible.
202
494001
3835
je da dobijete
što je više konteksta moguće.
što je više konteksta moguće.
Dakle, radi se o tome da umanjite sliku
sa jednog podataka,
sa jednog podataka,
08:29
So it's about zooming out
from one data point,
from one data point,
203
497860
2191
08:32
like the unemployment rate
is five percent,
is five percent,
204
500075
2104
na primer, stopa nezaposlenosti je 5%
08:34
and seeing how it changes over time,
205
502203
1757
i vidite kako se menja tokom vremena,
08:35
or seeing how it changes
by educational status --
by educational status --
206
503984
2650
ili da vidite kako se menja
s obzirom na status obrazovanja -
s obzirom na status obrazovanja -
08:38
this is why your parents always
wanted you to go to college --
wanted you to go to college --
207
506658
3104
zato su roditelji uvek želeli
da idete na fakultet -
da idete na fakultet -
ili da vidite kako varira
s obzirom na pol.
s obzirom na pol.
08:41
or seeing how it varies by gender.
208
509786
2032
Danas je stopa nezaposlenosti
muškaraca viša
muškaraca viša
08:43
Nowadays, male unemployment rate is higher
209
511842
2127
nego stopa nezaposlenosti žena.
08:45
than the female unemployment rate.
210
513993
1700
Do ranih '80-ih godina, bilo je obrnuto.
08:47
Up until the early '80s,
it was the other way around.
it was the other way around.
211
515717
2695
Ovo je priča o jednoj od najvećih promena
08:50
This is a story of one
of the biggest changes
of the biggest changes
212
518436
2117
koja se dogodila u američkom društvu.
08:52
that's happened in American society,
213
520577
1720
i sve je na tom grafikonu,
kada sagledate stvari izvan proseka.
kada sagledate stvari izvan proseka.
08:54
and it's all there in that chart,
once you look beyond the averages.
once you look beyond the averages.
214
522321
3276
Sve je u osama;
08:57
The axes are everything;
215
525621
1165
kada promenite nivo sagledavanja,
možete promeniti priču.
možete promeniti priču.
08:58
once you change the scale,
you can change the story.
you can change the story.
216
526810
2669
09:01
OK, so the third and final question
that I want you guys to think about
that I want you guys to think about
217
529503
3380
U redu, treće i poslednje pitanje
o kojem želim da razmišljate
o kojem želim da razmišljate
kada posmatrate statističke podatke
09:04
when you're looking at statistics is:
218
532907
1819
je kako su podaci prikupljeni.
09:06
How was the data collected?
219
534750
1873
Do sada sam govorila samo o načinu
na koji se podaci saopštavaju,
na koji se podaci saopštavaju,
09:09
So far, I've only talked about the way
data is communicated,
data is communicated,
220
537667
2939
ali način njihovog prikupljanja
podjednako je bitan.
podjednako je bitan.
09:12
but the way it's collected
matters just as much.
matters just as much.
221
540630
2276
09:14
I know this is tough,
222
542930
1167
Znam da je ovo teško,
jer metodologija može biti nejasna
i nekako dosadna,
i nekako dosadna,
09:16
because methodologies can be opaque
and actually kind of boring,
and actually kind of boring,
223
544121
3081
ali postoje jednostavni koraci
pomoću kojih možete ovo proveriti.
pomoću kojih možete ovo proveriti.
09:19
but there are some simple steps
you can take to check this.
you can take to check this.
224
547226
2873
Ovde ću upotrebiti jedan poslednji primer.
09:22
I'll use one last example here.
225
550123
1839
09:24
One poll found that 41 percent of Muslims
in this country support jihad,
in this country support jihad,
226
552309
3887
Jedna anketa je otkrila da 41 odsto
muslimana u ovoj zemlji podržava džihad,
muslimana u ovoj zemlji podržava džihad,
što je očigledno prilično zastrašujuće
09:28
which is obviously pretty scary,
227
556220
1525
09:29
and it was reported everywhere in 2015.
228
557769
2642
i o tome se izveštavalo
svuda 2015. godine.
svuda 2015. godine.
09:32
When I want to check a number like that,
229
560435
2615
Kada hoću da proverim takvu brojku,
počeću pronalaženjem originalnog upitnika.
09:35
I'll start off by finding
the original questionnaire.
the original questionnaire.
230
563074
2501
Ispostavilo se da su novinari
koji su izveštavali o tom podatku
koji su izveštavali o tom podatku
09:37
It turns out that journalists
who reported on that statistic
who reported on that statistic
231
565599
2926
zanemarili pitanje nešto niže na anketi
09:40
ignored a question
lower down on the survey
lower down on the survey
232
568549
2231
koje je pitalo ispitanike
kako definišu „džihad“,
kako definišu „džihad“,
09:42
that asked respondents
how they defined "jihad."
how they defined "jihad."
233
570804
2346
a većina njih ga je definisala
09:45
And most of them defined it as,
234
573174
1981
kao „ličnu, mirnu borbu muslimana
da budu religiozniji“.
da budu religiozniji“.
09:47
"Muslims' personal, peaceful struggle
to be more religious."
to be more religious."
235
575179
3942
Samo 16 procenata ga je definisalo
kao „nasilan sveti rat protiv nevernika“.
kao „nasilan sveti rat protiv nevernika“.
09:51
Only 16 percent defined it as,
"violent holy war against unbelievers."
"violent holy war against unbelievers."
236
579145
4194
To je zaista bitan deo;
09:55
This is the really important point:
237
583363
2430
09:57
based on those numbers,
it's totally possible
it's totally possible
238
585817
2155
na osnovu tih brojeva, sasvim je moguće
09:59
that no one in the survey
who defined it as violent holy war
who defined it as violent holy war
239
587996
3105
da niko ko ga je u istraživanju
definisao kao nasilni sveti rat
definisao kao nasilni sveti rat
10:03
also said they support it.
240
591125
1332
nije rekao i da ga podržava.
10:04
Those two groups might not overlap at all.
241
592481
2208
Te dve grupe se možda
uopšte ne preklapaju.
uopšte ne preklapaju.
Takođe, vredi pitati
kako je istraživanje sprovedeno.
kako je istraživanje sprovedeno.
10:07
It's also worth asking
how the survey was carried out.
how the survey was carried out.
242
595122
2637
Ovo je bilo nešto što se zove
opciona anketa,
opciona anketa,
10:09
This was something called an opt-in poll,
243
597783
1998
što znači da je bilo ko mogao
da je nađe na internetu i popuni je.
da je nađe na internetu i popuni je.
10:11
which means anyone could have found it
on the internet and completed it.
on the internet and completed it.
244
599805
3402
Nema načina da se sazna da li se ti ljudi
uopšte identifikuju kao muslimani.
uopšte identifikuju kao muslimani.
10:15
There's no way of knowing
if those people even identified as Muslim.
if those people even identified as Muslim.
245
603231
3339
Naposletku, u toj anketi
je bilo 600 ispitanika.
je bilo 600 ispitanika.
10:18
And finally, there were 600
respondents in that poll.
respondents in that poll.
246
606594
2612
U ovoj zemlji ima
približno tri miliona muslimana,
približno tri miliona muslimana,
10:21
There are roughly three million
Muslims in this country,
Muslims in this country,
247
609230
2654
prema Centru za istraživanje Pju.
10:23
according to Pew Research Center.
248
611908
1607
To znači da se anketa obraćala
otprilike jednom od svakih 5 000 muslimana
otprilike jednom od svakih 5 000 muslimana
10:25
That means the poll spoke to roughly
one in every 5,000 Muslims
one in every 5,000 Muslims
249
613539
2993
10:28
in this country.
250
616556
1168
u ovoj zemlji.
To je jedan od razloga
10:29
This is one of the reasons
251
617748
1266
zašto su vladini statistički podaci
često bolji od privatnih.
često bolji od privatnih.
10:31
why government statistics
are often better than private statistics.
are often better than private statistics.
252
619038
3607
Anketa se može obratiti
par stotina ljudi, možda hiljadu,
par stotina ljudi, možda hiljadu,
10:34
A poll might speak to a couple
hundred people, maybe a thousand,
hundred people, maybe a thousand,
253
622669
3035
ili ako ste Loreal i pokušavate da prodate
proizvode za negu kože 2005. godine,
proizvode za negu kože 2005. godine,
10:37
or if you're L'Oreal, trying to sell
skin care products in 2005,
skin care products in 2005,
254
625728
3058
10:40
then you spoke to 48 women
to claim that they work.
to claim that they work.
255
628810
2417
onda ste razgovarali sa 48 žena
da biste tvrdili da deluju.
da biste tvrdili da deluju.
10:43
(Laughter)
256
631251
1026
(Smeh)
10:44
Private companies don't have a huge
interest in getting the numbers right,
interest in getting the numbers right,
257
632301
3556
Privatne kompanije nemaju veliki interes
da dobiju ispravne brojeve,
da dobiju ispravne brojeve,
već su im samo potrebni
odgovarajući brojevi.
odgovarajući brojevi.
10:47
they just need the right numbers.
258
635881
1755
10:49
Government statisticians aren't like that.
259
637660
2020
Vladini statističari nisu takvi.
10:51
In theory, at least,
they're totally impartial,
they're totally impartial,
260
639704
2447
Makar u teoriji, sasvim su nepristrasni,
ne samo zato što većina njih
obavlja svoj posao
obavlja svoj posao
10:54
not least because most of them do
their jobs regardless of who's in power.
their jobs regardless of who's in power.
261
642175
3501
bez obzira na to ko je na vlasti.
Oni su državni službenici.
10:57
They're civil servants.
262
645700
1162
10:58
And to do their jobs properly,
263
646886
1964
A da bi valjano radili svoj posao,
ne govore samo sa par stotina ljudi.
11:00
they don't just speak
to a couple hundred people.
to a couple hundred people.
264
648874
2363
Oni brojevi vezani za nezaposlenost
na koje se uporno pozivam
na koje se uporno pozivam
11:03
Those unemployment numbers
I keep on referencing
I keep on referencing
265
651261
2318
11:05
come from the Bureau of Labor Statistics,
266
653603
2004
su iz odeljenja za statistiku
Ministarstva za rad,
Ministarstva za rad,
11:07
and to make their estimates,
267
655631
1335
a da bi izvršili svoje procene,
11:08
they speak to over 140,000
businesses in this country.
businesses in this country.
268
656990
3489
oni se obraćaju
preko 140 000 firmi u ovoj zemlji.
preko 140 000 firmi u ovoj zemlji.
11:12
I get it, it's frustrating.
269
660503
1725
Kapiram, to frustrira.
Ako želite da proverite podatke
koji dolaze iz privatne kompanije,
koji dolaze iz privatne kompanije,
11:14
If you want to test a statistic
that comes from a private company,
that comes from a private company,
270
662252
3115
možete da kupite kremu za lice
za sebe i gomilu prijatelja, isprobate,
za sebe i gomilu prijatelja, isprobate,
11:17
you can buy the face cream for you
and a bunch of friends, test it out,
and a bunch of friends, test it out,
271
665391
3361
a ako ne deluje, možete reći
da su brojevi bili pogrešni.
da su brojevi bili pogrešni.
11:20
if it doesn't work,
you can say the numbers were wrong.
you can say the numbers were wrong.
272
668776
2591
Međutim, kako da preispitate
vladine podatke?
vladine podatke?
11:23
But how do you question
government statistics?
government statistics?
273
671391
2146
Samo uporno sve proveravajte.
11:25
You just keep checking everything.
274
673561
1630
Saznajte kako su prikupili brojeve.
11:27
Find out how they collected the numbers.
275
675215
1913
Otkrijte da li na grafikonu vidite
sve što treba da vidite.
sve što treba da vidite.
11:29
Find out if you're seeing everything
on the chart you need to see.
on the chart you need to see.
276
677152
3125
Ali, ne odustajte sasvim od brojeva,
jer ako odustanete,
jer ako odustanete,
11:32
But don't give up on the numbers
altogether, because if you do,
altogether, because if you do,
277
680301
2965
donosićemo odluke
o javnoj politici u neznanju,
o javnoj politici u neznanju,
11:35
we'll be making public policy
decisions in the dark,
decisions in the dark,
278
683290
2439
isključivo koristeći
lične interese kao smernice.
lične interese kao smernice.
11:37
using nothing but private
interests to guide us.
interests to guide us.
279
685753
2262
Hvala.
11:40
Thank you.
280
688039
1166
(Aplauz)
11:41
(Applause)
281
689229
2461
ABOUT THE SPEAKER
Mona Chalabi - Data journalistMona Chalabi tries to take the numb out of numbers. She's left with lots of "ers."
Why you should listen
After working for a humanitarian organisation, Mona Chalabi saw how important data was, but also how easily it could be used by people with their own specific agendas. Since then, her work for organizations like Transparency International and The Guardian has had one goal: to make sure as many people as possible can find and question the data they need to make informed decisions about their lives.
Chalabi is currently the Data Editor of the Guardian US, where she writes articles, produces documentaries and turns data into illustrations and animations. In 2016, her data illustrations were commended by the Royal Statistical Society.
Mona Chalabi | Speaker | TED.com