Social science and government aims
Proposed standards for
public goals
and research aggregating statistics on individuals
Matt Berkley
Draft, 10 January 2006
Contents
Summary
Purpose of this document
Background
Preliminary notes
Proposed standards for goals and research
1. Estimate reliability of the
data.
2. Estimate reliability of
conclusions.
3. Distinguish data on samples
from inferences on populations.
4. Distinguish reports from
history.
5. Distinguish population trends
from trends for people.
6. Distinguish spending from
income.
7. Distinguish spending from
consumption.
8. Distinguish consumption from
adequacy.
9. Distinguish income from profit.
10. Distinguish prices from relevant prices.
11. Distinguish prices from cost of living.
12. Distinguish consumption gains from material gains.
13. Distinguish incidence from prevalence.
14. Distinguish prevalence from degree.
15. Distinguish material conditions from judgements on well-being.
16. Imagine real people.
17. Look at meaningful groups of
people.
18. Distinguish “statistical
significance” from importance.
General notes on the standards
Suggested examination questions
This document proposes minimum
standards in the use of language for
a) some kinds
of public policy goals and
b) some kinds of reporting in social science.
Some of the standards may be considered
ethical standards.
The proposals are for the attention of
researchers, funders and policy makers and the general public.
The document may also be a guide to
asking some kinds of questions on past claims about poverty and
prosperity.
The document mentions potential, and
some actual, errors in social science.
The impetus was the author’s interest
in current methods of economic analysis.
In the process he developed an urge to understand the relationships
between
a) current theory in social science
b) predominant
practice in social science and government and
c) what he
thought he might like as government aims and progress indicators if he were
among those called “extremely poor”.
Thinking about the extreme case
sometimes reveals the principles.
The aim of this document is to help bring
clarity to both documents and debates on some aspects of government policy, and
some aspects of social science reporting.
Some of the standards apply in particular to economics.
Many people die of malnutrition in a world
where resources are adequate. At the
same time, people differ as to what they mean when they say “poverty has got
worse” or “poverty has got better”.
An element of reasoning behind this
document is: One part of a solution to
malnutrition may be for organisations to adopt common language. The organisations would include
professional organisations, institutions and funders, including government
agencies.
The reasoning stems from the apparent fact
that not only consumers of economic information but sometimes producers appear
to have been unaware of the precise nature of the information.
Some questions related to social science
are fundamentally subjective. That is
one reason why the document is aimed at a wider readership than academics.
The document may help clarify which are
scientific matters, which are matters of opinion and which are moral
matters.
The distinctions in this document may help
clarify the evidential position on some claims concerning human poverty and
prosperity.
One argument for adopting such standards
may be: Looking at what lies behind the
language of social scientists may help solve some puzzles in international
statistics.
If public institutions adopt standards for
clearer language, the public may be in a better position to choose between
policies.
In 2000 the present author raised a
fundamental flaw with professors of economics:
if the “poorest” die, the figures appear to show they did better. Since then, academics have begun
discussing that issue, and some have written that this is a significant
conceptual advance. But in this area, as
in others, there are no rules for economists -
no boundaries for acceptable professional conduct. What is needed are rules forbidding social
scientists from making statements for which they clearly do not have evidence
and which may have important social impacts.
It seems wrong that social scientists can make elementary errors and
face no sanction from either employers or professional organisations.
For instance, if a doctor recommended a
treatment without looking at survival rates, that might be classed as a serious
mistake. If a social scientist
recommended a policy without looking at survival rates, that might be classed
as a far more serious mistake. There
is less point in spending money training social scientists if governments can
employ them to say whatever they like.
At present, the author knows of no
political party which endorses such rules in relation to the use of economic
data.
In 2000 it struck me that the debate over
global poverty needed better statistics.
I came to realise it needed clarity about existing statistics.
I was a trustee of a family trust. It occurred to me that the trust might help
provide statistics for the debate on global poverty. I also thought it might help bring academics
together with campaigners.
I then read an economic policy document taken
seriously by newspapers and the UK government, which contained elementary
errors. This situation seemed to
indicate widespread errors of reasoning in economics.
Around the same time, the heads of several
campaigning and research organisations told me that it would be a good thing if
there were a think tank on global poverty.
Perhaps I was aiming to clarify two
things:
1) how existing
policy and research methods related to what I might want for myself in the
situation of the “poorest”;
2) the evidence
for the most influential claims about policies and poverty.
Some of the issues go back a long way.
For example, the tradition in
macroeconomics (large-scale economics) has been to assume that if incomes of
the poorest rose 1% they had a 1% benefit.
Adam Smith noted a difference between the
inflation rate for working-class people and the national rate in 1776.
He also wrote about needs being a factor in
prosperity: he did not advocate the idea
that resources measured either prosperity or poverty without reference to
need.
Another example relates to adding up the
numbers. Economists often write about utility - meaning the consequences for people. The idea of calculating “utility” or gains in
well-being to people goes back to the philosopher Jeremy Bentham. His form of “utilitarianism” was about “the
greatest good to the greatest number”.
He included duration of pleasure as a factor in his “calculus” of
consequences. But economists for some
reason almost all ignored this in making claims about poverty.
It appears to have been common in
macroeconomics to claim to know economic benefits to the poorest people without
thinking about prices, needs or survival rates.
There is disagreement over what prosperity
is; what might
constitute evidence for prosperity; what
might be accurately described as past trends in prosperity or its inverse,
poverty; what are better policies for
increasing prosperity.
It may be that clearer language would help
resolve some aspects of disagreement.
It is important to bear in mind that the
standards are not meant to override common sense.
What counts as common sense is a subjective
matter. There are inevitably areas
related to these standards where judgement is involved. The aim is to clarify descriptions of the
evidence and the arguments used for the conclusion.
Science is about better approximating to
the truth.
The document aims to further that
enterprise.
In the real world, it is worth remembering
that there may be temptations for social scientists, employers, governments,
state institutions, media organisations, political organisations, and/or people
in general to believe what they want to believe, and/or try to make others
believe things without really having the evidence they think or say they
have.
This document aims to engage with that
aspect of the real world.
Suppose a social scientist says they
have data on a social trend. How do you
decide whether
to believe them?
One type of question you can ask is about
definitions. It’s often a good idea to
ask what they mean.
Another type of question is about
reliability.
An example of a question to ask is:
Do the numbers come from samples of the
population, or everyone?
Usually, numbers come from samples.
This kind of thing may seem at first a
complex area, but if you apply imagination you can think of some relevant
aspects you might want to ask about.
After all, the scientist has obtained the data from somewhere, and
scientists are only people.
Another example of an area where
questioning may be useful is this:
A scientist might say they have
measured something, when the reality is that they have added up answers people
gave.
For example, suppose a researcher says
“people are 2% happier in country X than in Y”.
A description of the procedure might be
something like this:
“In each country, workers asked one in
ten thousand people how happy they were.”
In a research project there may many
details. But the principles are often
easy to understand. In this case,
without knowing anything about the research, we can do some thinking. Here are some initial thoughts that we might
have:
1) You can’t really measure happiness - you can’t really know what people are
experiencing.
2) Costs are likely to limit the
sample, which may be one in 1000 people or fewer
3) Some people may not tell the truth
4) People may feel different at
different times
5) The questions might seem different
in different languages
6) The researchers might have been
forced to leave people they couldn’t find, or who wouldn’t answer
Some of these factors might have skewed
the results.
This example is not meant to reflect
the reality of research on what people say about happiness. It is to illustrate the principle that you
can think about what social scientists tell you, and the principle that it’s
not magic - it’s just people finding out about things by
methods that are possible in real life.
My personal belief is that in the case
of what some people call “extreme poverty” a strange kind of psychological
process has happened whereby the “non-poor” can have a kind of short-circuit of
imagination. Perhaps this is common
in other areas of social science research as well. What I have consistently observed is that
some people paid highly to be experts in “poverty” often fail to show evidence
of having thought about some of the most basic aspects of real life. Some of these aspects are mentioned below. They include questions about the reliability
of data.
Proposed standard for researchers:
Estimate reliability of data for the
specific purpose, giving reasons.
Estimates should be in the context of
the specific statistical tests.
Data may be reliable enough for a
simple test but not for a complex test.
This may sound complex, but the
principles are simple: think about
a) what you
are comparing and
b) how your
confidence in the data might stand these comparisons.
Example:
“We estimate that
in the context of
comparing outcomes under policy X with outcomes under policy Y,
taking into account
the number of countries in each category or being correlated,
the sample sizes,
the survey coverage,
the possible gaps in data,
the number of time periods,
[...and any other relevant factors...]
the likelihood of our figures being right to within a% is b.”
Note that the unreliability of a series
of inferences is multiplied (see below).
Researchers must consider the
reliability of inferences, giving reasons where the inferences are not
otherwise clear.
The unreliability of a series of
inferences is multiplied.
For example:
If
there are two steps in my argument
and
each has 70% probability
then
I am probably wrong.
70/100 x 70/100 = 4900/10000 = 49%.
Suppose survey data are on answers
about spending. This is mostly the
case in real life.
If an economist is asked by a
politician to say something about how well or badly poor people did, here would
be some necessary inferences.
From
i) “what poor people sampled
in 1990 and 2000 said they spent”
to
ii) “what representative
samples of poor people in 1990 and 2000 said they spent”
to
iii). “what poor people spent in 1990 and 2000”
to
iv) “trends
in spending for real poor people over time
(taking demographic change into account)”
to
v) “trends
in income for poor people”
or “consumption trends for poor people”
to
vi) “consumption
adequacy for real poor people”
to
vii) “material
gains for real poor people”
to
viii) “gains
in well-being for real poor people”.
Necessary assumptions might concern at
least, not necessarily in this order:
a) sample
adequacy
b) truthfulness
of respondents
c) memory of
respondents
d) demographic
change (which determines food needs)
e) workload
(which determines food needs)
f)
changing food quality
g) food prices
h) survival
rates
i) a theory of how well-being
relates to spending
j) the
relative value to people of assets and consumption.
In order to understand the inferences
it is perhaps important to be clear about different kinds of statements.
The statement
“People in our sample in country P had
rises in X”
is not the same statement as
“In country P, X rose”.
Researchers should note non-responses
and difficulties in obtaining random samples.
The aim here is to exclude the risk of error through sample bias.
Note:
The “rich” and the “destitute” may not be reachable in surveys. If the destitute are unreachable, it is not
clear how an economist can have data on the poorest.
Note on units chosen: If the units chosen for comparison are
countries, reasons should be given as to why these countries might constitute
representative samples of all relevant countries.
Similar considerations apply to time
periods.
What people said does not tell you what
they did.
Economic data on most people are on
their answers to questions about spending.
In many areas of life, answers people
give may not be true.
These areas of life include what they
spent, earned, ate, drank, used, or acquired.
Reasons relate to
a) honesty
b) self-deception
c) memory
d) mathematical
ability.
It is a mistake to describe answers as
measured quantities without good reason.
Reason:
To minimise risk of damage to people
from confusion of:
a) trends for
people
and
b) changes in
aggregates for populations
Example:
“the average
rise [for people] was x%”
versus
“the
[population] average rose by y%”.
Demography axiom
It is not possible to aggregate
outcomes from statistics solely on the living.
Note
Statistics on survivors are
selective. Aggregate outcome statistics
include those who die.
Statistics on survivors do not yield
data on what happened to people during a period.
Nor do they yield data on what happened
to the survivors over the period.
The statement
“The average was x% higher in 2000 than
in 1990”
does not tell a researcher either that
“people had
rises of x%
or that
“survivors had
rises of x%”.
Examples of what are strictly speaking
errors:
a) Any inferences as to aggregate
trends for people from United Nations Millennium Goal indicators for hunger,
poverty, education, AIDS, water.
These indicators were in terms of
population proportions. They are
therefore not indicators of aggregate progress for people.
Numbers of living people depend on
births, deaths and migration as well as trends for individuals.
Proportions of living people either
side of a line described in terms of proportions of people depend on births,
deaths and migration of people on each side of the line as well as on individual
trends.
b) Policy assessments from
macroeconomic statistics.
Treatment of population statistics as
statistics on people has been the dominant tradition in macroeconomics.
Exceptions to the standard: where there is
a) specific
relevant information on survival rates, age structure, birth rates and
migration
or
b) clear
reason to infer survival rates, age structure, birth rates and migration were
within reason and in all material respects constant, proportional or
irrelevant.
Survival axiom
Where survival rates are not within
reason known to be constant, proportional or irrelevant, the notion of an
average or other single-statistic aggregate outcome is not applicable.
Note
The standard is to cover cases where
either
a) a direct
statement is made concerning aggregate outcomes or
b) a reader
might reasonably understand the claim to refer to aggregate outcomes.
An example of type (b) would be where
the speaker refers to “reduction” of a condition considered undesirable and the
context is of alleviating the condition for sufferers.
Reason
In the period 1945-2005 most economic
data on individuals related to spending; yet in 2005 the tradition in
macroeconomics in summaries for the public was to describe the data as
“income”.
Risk : The public might
assume economists counted both savings and spending.
Globally, the data mostly related to
spending. A minority of data were on
income (notably in Latin America). Some
data were on the money value of items eaten or used.
Most of the numbers were on what a
sample of people said they spent.
Examples of what are strictly speaking
errors:
a) Any reference to Millennium Goal indicators
as on “income”.
Millennium Goal Indicator 1 is mostly
concerned with reducing the proportion of low spenders.
b) Any reference to the economic data
in policy assessments, as “income”.
For example, even if there were no
other problems, a claim by economists that “poor people’s incomes rose at the
same rate as policy X” would still be misleading. More accurate would be “poor people’s
spending rose with policy X”.
Terminological issue: The fact that the international data are a mixture
poses a linguistic problem for researchers:
how to describe the data accurately and concisely.
“Income” is misleading. It would be more accurate to describe the
data as on spending. In order not to
mislead, it might be better for economists to use the term “the economic
data”.
Purpose of standard
To ensure the phrase “consumption
expenditure” is shortened to “spending” rather than “consumption”.
Note
The word “consumption” would perhaps
most naturally be taken by a non-economist to refer to “items or services
received or used”.
In this sense, consumption would be
“things received”, not “money spent”.
An economist who had data on spending and who then wrote “consumption
has risen” would not be misleading the public in the same way as one who wrote
“poverty has fallen” or “incomes have risen” but would still be misleading
them.
The definition of what counts as
consumption is in any case perhaps in some ways arbitrary.
Example: Food consumption.
Food consumption adequacy for daily
tasks depends on at least:
size,
age,
economies of scale,
workload type,
workload amount,
weather,
food balance,
food quality.
Examples of error
a) Macroeconomic claims on global poverty.
Per-person statistics used by World
Bank, UN and others up until 2005 failed to take into account that the
proportion of children is falling.
b) Policy assessments from
macroeconomists based on per capita statistics.
Proportions of children are not
constant across countries, or within countries by
spending level.
In both cases inferences from
consumption to adequacy were made without reference to needs.
It is difficult to see how a researcher
who has not estimated needs for fuel, food, water, medicine, rent or other
basic items could have estimated poverty.
Real-world puzzles potentially
partially explained by this error:
i) Discrepancy between Food and Agriculture Organisation
reported hunger trend and World Bank reported poverty trend for Millennium
Goals.
ii) Discrepancy between protesters’ and
economists’ views of progress of global poorest.
iii) Discrepancy between World Bank and
other reports of progress on Millennium Goals.
Note:
In some periods of history falls in child-adult ratios (which make
cross-sectional per capita statistics, other things being equal, overestimate
progress) may coincide with rises in longevity (which make cross-sectional
statistics, other things being equal, underestimate progress).
Note on the concept of adequacy
Consumption adequacy is a concept
rather than a scientific variable.
For example, it might be argued that
people in a country where family members live further apart need more money for
transport.
How much fuel is needed in a cold place
to get to the same standard of living as in a hot place?
Such needs, as with many things
described as needs, are matters of opinion, not science.
See below on inflation and cost of
living for a related distinction.
Reason
Risk of inferring gains without
estimating needs.
Example
Inferences from
“income” to “income poverty”.
Profit axiom
(Income) -
(necessary outgoings)
= (profit).
Parallel
Businesses. The axiom applies to
a household as for a business.
For both businesses and households, the
following are true:
Revenue, income or turnover
are not profit.
1% more turnover does not indicate 1%
more profit.
An income rise of 1% does not measure
an income gain of 1%.
Note
It is not clear what philosophical
argument might be advanced that income is an indicator of welfare.
It does not measure the cost of rent,
childcare, transport, fuel, food, water or medical services in any
country. It is a measure of money
going round the system.
Income is a social indicator.
Reason for standard:
Common assumption
in macroeconomics that adjusting for national prices is adequate to show
economic benefits for the “poorest”.
Examples of error:
Use of data on the
“distribution of income” to infer consumption gains to people on low incomes.
One error in this procedure is that
data are usually spending, not income.
Secondly this is a theoretical
distribution in a model which does not take demographic change into account
(strictly speaking, confusing distribution among
people at different times with
distribution to real people over a period);
Thirdly, this procedure also confuses
prices with costs (more sensibly, costs = prices multiplied by needs - see below).
Also, the distribution of income would
not measure the distribution of price changes.
Cost of living axiom
Cost of living = prices x needs.
The above axiom does not imply that
either needs or the cost of living are measurable. It is a conceptual axiom, not a measurement
axiom.
In conceptual terms, the cost of living
is not simply a function of (dependent on) prices.
Statements implying “statistics were
adjusted for prices” to be distinguished from statements implying “cost of
living has been taken into account”.
Cost-of-living axiom
The cost of living an equivalent life
in different places,
at different times,
in different circumstances and/or
for different people,
insofar as it might be assessed in some respects objectively,
necessarily depends on both
a) prices
and
b) amounts
needed.
Note:
It follows that inflation is not a
measure of the cost of living.
Where a researcher either states or
implies cost of living has been taken into account, the standard is to
apply.
Note
Claims concerning needs, or the
relative satisfaction of needs, are logically matters of opinion.
For example, suppose someone says
people have done 1% better economically in country X than in country Y.
They might talk about income, expenses,
prices of necessaries and luxuries, house prices, rent prices, and so on. But in relation to human well-being, the
cost of living surely means the cost of living an equivalent life.
Note on the literal cost of living
In the case of the literal cost of living - of staying alive -
there is still the question of “staying alive in what condition?”. This includes, for example, potential damage
to brain or other organs from malnourishment.
The fact that I eat 1% better this year
does not tell you that I am 1% better off.
I might have sold my land to pay for food.
(Note:
this is not meant to imply that either “I eat 1% better” or “I am 1%
better” make sense).
Parallel: Business.
Capital gains are relevant to both businesses and people. In both cases, to leave them out in inferring
material gains is a mistake.
Capital gains and losses, including
debts, are factors in a person’s control or potential control of material
resources. An assumption that capital
gains and losses were in proportion to and in same direction as profit is
merely an assumption, not a scientific finding.
In the case of people, there is the
additional question of what counts as an economic gain. This question persists even if assets and
other things related to legal control of resources are added in. It is not clear how a scientist who wishes
to come judgements about levels of economic gains can dodge the question of
what constitutes an equivalent life in different circumstances (see under cost
of living, above).
Example
Proportion of poor people cannot tell
me how many were poor during period.
Prevalence (for example the prevailing
number of cases now and a year ago) does not tell a researcher about incidence
(the number of incidents over the year).
It is the tradition in economics to
call prevalence “incidence”.
The tradition among statisticians and
medical researchers is to talk of the “prevalence” of hunger or disease when
referring to the same concept.
Harmony of
language in this respect between economics and other social sciences might be
useful.
Incidence can rise and prevalence fall
if more die.
The number of rich people does not say
how rich they are.
In purely physical terms, material
inputs are not the same thing as bodily results.
A consumption gain, of good food to a
hungry person, is not useful if they cannot absorb the food. That may depend on the state of their
digestive system.
At the other extreme, many people in
some countries overeat. More
consumption, clearly, does not show more well-being.
In terms beyond the physical -
in terms of happiness, fulfilment, and so on - it
is not clear why anyone would think material conditions show overall
well-being.
At the abstract level, there is a
philosophical question about where a dividing line might be between “my
material circumstances” and “the environment around me”. The abstract question arises because
things which are legally not mine but which I use (for example trees or hills)
may be functionally equivalent to legal possessions.
A reasonable line of argument might be
that this does not matter very much, since other aspects of prosperity - the
relative importance of life length, health, assets, debts, stability,
self-respect, consumption of particular items, variety and so on - are
subjective anyway.
Another line of reasoning might be
this: Arguments about whether people
have done “better” or “worse” under different policies are largely pointless,
since some important aspects of the human condition are not possible to
research.
One test of whether a social science
claim makes sense is to think about yourself in the
same position as a subject of the research.
Suppose you decide you would not apply
the method to yourself. You might then
reasonably decide that not to make believe the claim about someone else. This technique can be used for many types
of inferences.
That test is about thinking about yourself as one person.
Imagination can also be used in relation to adding up outcomes. It is often sensible to imagine a smaller
unit than the research is about. For
instance, suppose the claim is about a country. You can imagine a village or a street or a
family. If the claim is about
comparing what happens to people in different countries, you can imagine people
in different houses, or streets or villages.
This is really concerned with a frame of mind. If a researcher is talking about
“consumption” then you are unlikely to grasp what relationship this may have to
the real world unless you can imagine how it applies to one person.
Sometimes imagining people at the
extremes helps bring out the basic principles.
When social scientists use abstract
nouns, it is wise to see how they apply to real life.
It is a good idea not to accept what
abstract nouns are supposed to be telling you unless you are clear about:
a) what the
researcher means them to refer to in real life and
b) in what
ways you think that does or does not make sense.
A fundamental principle in statistics
is this:
Conclusions about how one thing
correlates with another are not sensible if there is no clear cut-off point for
the groups.
The problem is this:
Apparently significant differences
might be caused by people just inside and/or just outside the group being
looked at.
If a researcher says “people who are X
have a higher trend than people who are Y” it is worth asking whether the
division makes sense.
Statistical significance does not tell
you a thing is important.
Suppose a medical researcher finds that
a procedure has a statistically significant association with outcomes.
This does not tell you the procedure is
significant in real life.
“Statistical significance” just means
that the association seems, given the researcher’s assumptions, to be unlikely
to be due to chance. It can still be a
small effect, even if all the researcher’s assumptions are correct.
For example, if 61% of people taking a
medicine lose their symptoms and 60% of those without the medicine lose the
symptoms, the association can still be statistically significant.
So if you read a medical research paper
which says a medicine is effective, an early question might be “how effective?”.
Note:
Another problem which may arise in relation to claims about medical
research is this: Comparing a medicine
to a placebo (fake treatment) is not the same as comparing it to no
treatment.
In the placebo case, according to
standard practice the patient agrees to the following: they are to get something which may or may
not be the real treatment.
A problem with this procedure is that
the patient uses up time and energy on it (going to the doctor, remembering to
take the pill or whatever it is, and thinking about whether they have got the
real treatment). A “no-treatment”
condition would be different from this.
If the researcher sent some people away in the knowledge that they were
not being given the true treatment, they would perhaps do something else.
Another problem with placebos as
comparison conditions is that they are something which the patient knows may
not be the real treatment. A true
placebo would be one which the researcher said would work, or at least was
definitely the preferred treatment.
Possible objection
“Some distinctions are not relevant, or not important in practice in discipline A or
type of research B”.
Answer
In any area of work where there are
reasonably foreseeable circumstances in which the distinctions may become
important, it seems logical that they should be considered seriously.
Where a social scientist has information on
variable x, and wishes to say something about variable y or express an opinion
about z, the burden of proof is on the scientist to show the logic of the
inference.
Note on types of argument
It can be useful to ask what exactly a
writer is saying about economic data.
It is important for scientists -
and anyone reading conclusions by scientists - to
distinguish between
a) calculation,
b) inference,
c) hypothesis,
d) conjecture and
e) opinion.
Calculation, or deduction, is where
something must be true if other things are true.
Inference is where you come to a conclusion
because it seems sensible.
Hypothesis is where you provide what seems
a sensible explanation of the evidence.
Conjecture is a guess about either a
conclusion or explanation.
Opinion, rather than knowledge, is what is
possible to give about some matters. In
the economic sphere, these include
a) the relative importance of assets,
debts, community assets, environmental assets, life length, culture, health,
leisure time, working conditions, working hours and other aspects of economic
conditions to real people;
b) what people
need.
It is also important to understand
a) where
statistics come from and
b) their
limitations compared to broader aspects of real life you may think important.
Context of the standards
The proposed standards are not sufficient
to ensure that statements on statistics, even in the limited areas covered
here, reflect real life adequately.
The aim has been to identify some conditions under which descriptions of
research may better reflect real life
- in aspects which are likely to
make significant differences to people’s lives.
These proposed standards form a small
subset of those which might be proposed.
One response to these proposals might
be “but statistics can be misdescribed in many other
ways in any case” - which is true. That would be a good starting point for
further standards.
For school courses in social science
1. What is the difference between “the
average rise” and “the rise in the average“?
2. How is this difference relevant to people who are ill, elderly or poor?
3. If a statement depends on three
assumptions each of 80% probability, what is the likelihood of it being right?
For philosophy courses (moral
philosophy and philosophy of social science):
4. Under what circumstances would you
consider the following a true statement?
“I have measured your prosperity”.
5. Under what circumstances would you
consider the following a true statement?
“Poverty has fallen by more in country
X than in country Y”.
6. Under what circumstances would you
consider the following a true statement?
“Policy X is better for the poor than
policy Y”.
7. How might the
question “how many poor people are there?” be answerable if the question “how
many rich people are there” is
not?
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