Unimportant speak so to be not on the flip side By Prof Dr Sohail Ansari

 "YOU don’t choose your life; it chooses you. There’s no point asking why life has reserved certain joys or griefs, you just accept them and carry on."- Adultery, Paulo Coelho It's forbidden to try to imagine God (i.e. to give him a specific appearance), He is like no one, and nothing resembles Him. We know Him by contemplating in His signs and creatures, and by information revealed in the Qur'an and the Sunnah [sayings of Prophet Muhammad (peace be upon him)]. Our understanding of Allah is to be derived from no other sources beyond these two (i.e. The Qur'an and Sunnah).

 It is not always about the subject matter, softer can look harder if heavily reported 

·      Important event speaks for themselves and you notice, Journalists make unimportant ones speak so that they notice you.

Do you know the difference between a beautiful woman and a charming one? A beauty is a woman you notice, a charmer is one who notices you. Adlai E. Stevenson

Association

When two variables are related, we say that there is association between them.

For example, consider the height, X, and weight, Y, of a sample of school children. Tall children tend to be heavier, so high values of X are associated with high values of Y. The correlation coefficient describes the amount of linear association between two such numerical variables.

Causal relationships

In some data sets, it is possible to conclude that one variable has a direct influence on the other. This is called a causal relationship.

For example, ...

·       A scientist in a dairy factory tries four different packaging materials for blocks of cheese and measures their shelf life. The packaging material might influence shelf life, but the shelf life cannot influence the packaging material used. The relationship is therefore causal.

·       A bank manager is concerned with the number of customers whose accounts are overdrawn. Half of the accounts that become overdrawn in one week are randomly selected and the manager telephones the customer to offer advice. Any difference between the mean account balances after two months of the overdrawn accounts that did and did not receive advice can be causally attributed to the phone calls.

If two variables are causally related, it is possible to conclude that changes to the explanatory variable, X, will have a direct impact on Y.

Non-causal relationships

Not all relationships are causal. In non-causal relationships, the relationship that is evident between the two variables is not completely the result of one variable directly affecting the other. In the most extreme case, ...

Two variables can be related to each other without either variable directly affecting the values of the other.

The two diagrams below illustrate mechanisms that result in non-causal relationships between X and Y.

If two variables are not causally related, it is impossible to tell whether changes to one variable, X, will result in changes to the other variable, Y.

For example, the scatterplot below shows data from a sample of towns in a region.

The positive correlation between the number of churches and the number of deaths from cancer is an example of a non-causal relationship -- the size of the towns is a lurking variable since larger towns have more churches and also more deaths. Clearly decreasing the number of churches in a town will not reduce the number of deaths from cancer!

Researchers usually want to detect causal relationships

A causal relation between two events exists if the occurrence of the first causes the other. The first event is called the cause and the second event is called the effect. A correlation between two variables does not imply causation. On the other hand, if there is a causal relationship between two variables, they must be correlated.

Example:

A study shows that there is a negative correlation between a student's anxiety before a test and the student's score on the test. But we cannot say that the anxiety causes a lower score on the test; there could be other reasons—the student may not have studied well, for example. So the correlation here does not imply causation.

However, consider the positive correlation between the number of hours you spend studying for a test and the grade you get on the test. Here, there is causation as well; if you spend more time studying, it results in a higher grade.

One of the most commonly used measures of correlation is Pearson Product Moment Correlation or Pearson's correlation coefficient. It is measured using the formula,

r x y = n ∑ x y − ∑ x ∑ y ( n ∑ x 2 − ( ∑ x ) 2 ) ( n ∑ y 2 − ( ∑ y ) 2 )

The value of Pearson's correlation coefficient vary from − 1 to + 1 where –1 indicates a strong negative correlation and + 1 indicates a strong positive correlation.

Yolanda has taught college Psychology and Ethics, and has a doctorate of philosophy in counselor education and supervision.

This lesson explores the relationship between cause and effect and teaches you about the criteria for establishing a causal relationship, the difference between correlation and causation, and more.

Definition of Cause and Effect

Think about when you woke up today. In all likelihood, you were probably woken up by the sound of an alarm clock. The loud sound of the alarm was the cause. Without the alarm, you probably would have overslept. In this scenario, the alarm had the effect of you waking up at a certain time. This is what we mean by cause and effect.

A cause-effect relationship is a relationship in which one event (the cause) makes another event happen (the effect). One cause can have several effects. For example, let's say you were conducting an experiment using regular high school students with no athletic ability. The purpose of our experiment is to see if becoming an all-star athlete would increase their attractiveness and popularity ratings among other high school students.

Suppose that your results showed that not only did the students view the all-star athletes as more attractive and popular, but the self-confidence of the athletes also improved. Here we see that one cause (having the status of an all-star athlete) has two effects (increased self-confidence and higher attractiveness ratings among other students).

Cause-Effect Criteria

In order to establish a cause-effect relationship, three criteria must be met. The first criterion is that the cause has to occur before the effect. This is also known as temporal precedence. In the example above, the students had to become all-star athletes before their attractiveness ratings and self-confidence improved. For example, let's say that you were conducting an experiment to see if making a loud noise would cause newborns to cry. In this example, the loud noise would have to occur before the newborns cried. In both examples, the causes occurred before the effects, so the first criterion was met.

Second, whenever the cause happens, the effect must also occur. Consequently, if the cause does not happen, then the effect must not take place. The strength of the cause also determines the strength of the effect. Think about the example with the all-star athlete. The research study found that popularity and self-confidence did not increase for the students who did not become all-star athletes. Let's assume we also found that the better the student's rankings in sports; that is, the stronger they became in athletics compared to their peers, the more popular and confident the student became. For this example, criterion two is met.

Let's say that for our newborn experiment we found that as soon as the loud noise occurred, the newborn cried and that the newborns did not cry in absence of the sound. We also found that the louder the sound, the louder the newborn cried. In this example, we see that the strength of the loud sound also determines how hard the newborn cries. Again, criterion two has been met for this example.