Re: Segregated workplaces
**Very well.
You are sounding shah Se Barh Ke Shah Ke Wafadaar now.
Here is why.
The article itself says.**
"About the Spring 2010 Pew Global Attitudes Survey
Results for the survey are based face-to-face interviews conducted under the direction of
Princeton Survey Research Associates International. All surveys are based on national
samples except Pakistan, where the sample was disproportionately urban.
The descriptions below show the margin of sampling error based on all interviews
conducted in that country.
For results based on the full sample in a given country, one can say with 95% confidence that the error attributable to sampling and other random effects is plus or minus the margin of error. In addition to sampling error, one should bear in mind that question wording and practical difficulties in conducting surveys can introduce error or bias into the findings of opinion polls."
This is exactly I had in mind before even read this portion. 
**There is such thing in statistics which is called “Sampling error” . This has to do with the selection of wrong sample of population from a large population. Kinda like this below what you posted earlier:
**
Factors that Affect Confidence Intervals
There are three factors that determine the size of the confidence interval for a given confidence level:
- Sample size
- Percentage
- Population size
Sample Size
The larger your sample size, the more sure you can be that their answers truly reflect the population. This indicates that for a given confidence level, the larger your sample size, the smaller your confidence interval. However, the relationship is not linear (i.e., doubling the sample size does not halve the confidence interval).
Percentage
Your accuracy also depends on the percentage of your sample that picks a particular answer. If 99% of your sample said “Yes” and 1% said “No,” the chances of error are remote, irrespective of sample size. However, if the percentages are 51% and 49% the chances of error are much greater. It is easier to be sure of extreme answers than of middle-of-the-road ones.
When determining the sample size needed for a given level of accuracy you must use the worst case percentage (50%). You should also use this percentage if you want to determine a general level of accuracy for a sample you already have. To determine the confidence interval for a specific answer your sample has given, you can use the percentage picking that answer and get a smaller interval.
So, here we have total sample size of 2000. If we assume ALL gave a particular answer, then the percentage would be 100% for the whole sample size.
We do not know how many actually answered a particular one.
**Plus, the ratio of sample size to whole population is too low (2000/180,000,000= 0.0000015) just as a common sense.
**
Population Size
How many people are there in the group your sample represents?
This may be the number of people in a city you are studying, the number of people who buy new cars, etc.
**Often you may not know the exact population size. **This is not a problem. The mathematics of probability proves the size of the population is irrelevant unless the size of the sample exceeds a few percent of the total population you are examining.
This means that a sample of 500 people is equally useful in examining the opinions of a state of 15,000,000 as it would a city of 100,000.
(This does not apply here, since we know the population and margin of error of +/- 3 is too high for sample size of 2000 only in such high poplulation which is being blamed of representing merely ‘some’ of 2000 sample size. Remember, not all of 2000 must have answered the same way)
For this reason, The Survey System ignores the population size when it is “large” or unknown. Population size is only likely to be a factor when you work with a relatively small and known group of people (e.g., the members of an association).
The confidence interval calculations assume you have a genuine random sample of the relevant population. If your sample is not truly random, you cannot rely on the intervals. Non-random samples usually result from some flaw in the sampling procedure. An example of such a flaw is to only call people during the day and miss almost everyone who works. For most purposes, the non-working population cannot be assumed to accurately represent the entire (working and non-working) population.**
We do know, that sample was not randomly selected as in Pakistan.**