It is not safe to be unpopular By Prof Dr Sohail Ansari
You can tell the size of a man by the size of
the thing that makes him mad. Adlai E. Stevenson Every
one of you is a shepherd and is responsible for his flock. The leader of the
people is a guardian and is responsible for his subjects: a man is the guardian
of his family and is responsible for his subjects, a woman is the guardian of
her husband’s home and of his children and is responsible for them, and the
slave of a man is a guardian of his master’s property and is responsible for
it. Surely, every one of you is a shepherd and responsible for his flock.
Source: Sahih Bukhari 6719,
Grade: Muttafaqun
Alayhi
Journalists will stop telling truth if people stop telling lies
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It is not safe to be unpopular. Popularity gives journalist a
licence to assault. Popularity owns a great deal to say something exactly
opposite to that is being circulated around. If people will stop telling truth, journalists
will stop telling lies.
‘My definition of a free society is a society where it is safe to be
unpopular’.
‘I will make a
bargain with the Republicans. If they will stop telling lies about Democrats,
we will stop telling the truth about them’. Adlai Stevenson
Types of
Relationships
A relationship refers to
the correspondence between
two variables. When
we talk about types of relationships, we can mean that in at least two ways:
the nature of the relationship or the pattern of it.
The Nature of a Relationship
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While all relationships tell
about the correspondence
or correlation between two variables, there is a special type of relationship that holds that the two variables are not only in correspondence, but that one causes the other. This is the key distinction between a simple correlational relationship and a causal relationship. A correlational relationship simply says that two things perform in a synchronized manner.
For instance, there has often
been talk of a relationship between ability in math and
proficiency in music. In general people who are good in one may have a greater
tendency to be good in the other; those who are poor in one may also tend
to be poor in the other. If this relatioship is true, then we can say that the two
variables are correlated. But knowing that two variables are correlated
does not tell us whether one causes the other.
We know, for instance, that there is a correlation between the number
of roads built in Europe and the number of children born in the United
States.
Does that mean that if we
want fewer children in the U.S., we should stop building so many roads in
Europe? Or, does it mean that if we don't have enough roads in
Europe, we should encourage U.S. citizens to have more babies? Of course not.
(At least, I hope not). While there is a relationship between the number of
roads built and the number of babies, we don't believe that
the relationship is a causal one. This leads to
consideration of what is often termed the third variable
problem. In this example, it may be that there is a third variable
that is causing both the building of roads and the birthrate, that is causing
the correlation we observe. For instance, perhaps the general world
economy is responsible for both.
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When the economy is good more roads are built in Europe and more
children are born in the U.S. The key lesson here is that you have to be
careful when you interpret correlations.
If you observe a
correlation between the number of hours students use the computer to study and their grade point
averages (with high computer
users getting higher grades), you cannot assume that the
relationship is causal: that computer use improves grades. In this
case, the third variable might be socioeconomic status -- richer students who
have greater resources at their disposal tend to both use computers and do
better in their grades. It's the resources
that drives both use and grades, not computer use that causes the change in the
grade point average.
Patterns of Relationships
We
have several terms to describe the major different types of patterns one might
find in a relationship. First, there is the case of no relationship at all. If you know
the values on one variable, you don't know anything about the values on the other. For instance, I suspect
that there is no relationship between the length of the lifeline on your hand
and your grade point average. If I know your GPA, I don't have any idea how long your lifeline is.
Then, we have the positive relationship. In a
positive relationship, high values on one variable are associated with high
values on the other and low values on one are associated with low values on the
other. In this example, we assume an idealized positive
relationship between years of education and the salary one might expect to be making.
These are the simplest
types of relationships we might typically estimate in research. But the pattern
of a relationship can be more
complex than this. For instance, the
figure on the left shows a relationship that change over the range of both
variables, a curvilinear relationship. In this example, the horizontal axis represents dosage of a drug for an illness
and the vertical axis represents a severity of illness measure. As dosage rises, severity of illness goes
down. But at some point, the patient begins to experience negative side effects associated
with too high a dosage, and the
severity of illness begins to increase again.
Negative
Correlation Examples
A negative correlation
means that there is an inverse relationship between two variables - when one
variable decreases, the other increases. The vice versa is a negative
correlation too, in which one variable increases and the other decreases. These
correlations are studied in statistics as a means of determining the
relationship between two variables.
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