Quota Sampling Disadvantage

In a random sample, every item has an equal chance of being selected. An example of quote sampling is that maybe we want to sample 200 people but choose 120 of them to be men and 80 of them women (maybe because those the the proportions of men and women in the population). Although the men and women separately may be picked at random, overall all people do not have an equal chance of being picked.

Thanks for the information. I now understand why quota sampling is not random. I would like to know, why quota sampling (due to being not random), is unable to assess sample error?

It is a bit unfair of them to write that, because you cannot be expected to calculate the sample error in Paper F2. However, if we have a truly random sample, then there are ways of calculating the error. What this means is that if, for example, in a sample 30% of people say they will vote for party X, then we are able to say something like 'in the population as a whole, the proportion who will vote for party X is 30% + or - 2%. The + or - 2% largely depends on how many were in the sample (obviously you will be less certain if the sample was only 50 people than if it was 500 people). However, again, you cannot be asked to do this. Its just that it is only possible if it is a truly random sample.

Noted. So, Is it alright to say that Random, Systematic, Stratified, Multistage & Cluster Sampling techniques are random whereas Quota Sampling is not random?

No - it is only random sampling that is completely random.