Answers

One problem with this sample is that people who read the Excite home page probably don't constitute a random sample of the population at large. Excite admits this: "This poll is a voluntary survey for our users, and is not scientifically projectable to any other population." However, there are good reasons to doubt that the results of the poll tell us much about Excite users, even. The people who take part in this poll are those who choose to, and there is no reason to think that people who choose to take part in the poll are a random sample of Excite users. In general, those people who have a strong opinion about the subject of the poll are far more likely to participate than those who are unsure. (Note how few people in such polls choose "Don't know" or the equivalent response.) This is an example of self-selection bias; if the participants in a sample are there because they choose to be, the sample is unlikely to be random.
One potential source of bias is the fact that people who don't own a telephone have no chance of being included in the sample. This was a big problem in the past. For example, a 1936 telephone poll predicted that Landon would overwhelmingly beat Roosevelt in the U.S. presidential election, but in fact the reverse occurred. In 1936, only wealthy people owned telephones, and wealthy people were more likely to vote for Landon. These days, this is not such a serious source of bias; however, those people with two telephone lines are twice as likely to appear in the sample as those with only one telephone line. A second potential source of bias is the fact that those people who are home to answer their phone are not necessarily a random sample of voters, particularly if the calls are made during the day, when many people are at work. A third potential source of bias is that some people will refuse to answer the interviewer's questions, and those who do answer may not constitute a random sample of the population. These latter two sources of bias are both instances of non-response bias; even if a truly random sample of people is polled, those who respond may not constitute a random sample.
Again, non-response bias may be a factor here. People who agree to respond to the interviewer's questions may be those who have strong views about redevelopment, or those who have a lot of time on their hands. People who are at home when the interviewer calls may be more likely to be old people or those with young children. Any of these are potential sources of bias.
People attending a blood donation centre may well not constitute a random sample of the population at large. For example, those who know that they have been exposed to hepatitis may choose not to give blood. This is a classic example of an opportunity sample--a sample which is chosen simply because it is easy to obtain. It is relatively easy to obtain blood samples for testing from people who have volunteered to donate blood; it would be much harder to persuade people chosen at random from the population to submit to a blood test. But a sample which is easy to obtain is not necessarily an informative sample.