So what you are looking out for is selection bias. When respondents have the choice to participate or not, their choice may be associated with their provided responses (I.e., only super satisfied or unsatisfied individual providing feedback). So if the sample was not random and unbiased, well then values may not reflect the super-population. Also, think of your sample size and the super-population size, with a small enough sample, with chance you may not get a representative signal. For example, a fair coin is a random variable with a 50-50 probability of heads or tails. Though, if a sample of flips is not large enough I can, by chance, get a long run of heads, making the coin look like its underlying function is not 50%, but higher. The larger the sample the closer it gets to converging to the truth. So if you have a small sample it may be hard to generalize to the population especially if it is not random.

Those are the issues you have to address when generalizing back to the population.