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The term "response rate" refers to the percentage of people who respond to a survey out of the total number of potential respondents. In other words, it's a measure of how many members of a sampled audience completed the survey. This statistic is particularly important for ensuring the reliability of survey results.
A low response rate, such as only 10 out of 100 people responding, raises concern about the data's representativeness. The smaller group of respondents may not accurately reflect the views or characteristics of the entire population intended to be studied. Here are some reasons why a low response rate might occur:

• Lack of interest in the survey topic
• Survey length that might be too time-consuming to complete
• Survey invitations might be overlooked or ignored

A low response rate reduces the reliability of survey results, as the views of a small, possibly unrepresentative portion of the population might dominate the findings.

A "volunteer sample" refers to a group of people who choose to participate in a survey or study on their own initiative. This means participation is entirely voluntary and not based on random selection. While volunteer samples are easy to acquire, they often introduce significant bias into research conclusions.
When individuals volunteer, they may be more interested or have strong feelings about the survey topic, which does not necessarily reflect the broader population's perspective. This creates a biased sample where specific views or characteristics dominate. For example, in a survey about dietary habits, those passionate about healthy eating might be more inclined to participate, skewing results. Ultimately, while volunteer samples can be convenient, the risk of bias can severely compromise the study's accuracy. The resulting data should be interpreted with caution, recognizing that it might not be generalizable to the entire population.

"Sampling bias" occurs when some members of a population are systematically more likely to be selected in a sample than others, leading to unrepresentative and skewed data. Both low response rates and volunteer samples can contribute to this bias. Sampling bias affects the accuracy and generalizability of survey findings.
In the case of a low response rate, sampling bias can occur if the respondents differ significantly from non-respondents in ways that affect the survey results. For example, if only older individuals respond to a survey about technology, the results may not accurately represent younger people's opinions or experiences. In a volunteer sample, the bias is even more inherent because participation is not random but self-selected. This self-selection often leads to an overrepresentation of individuals with a particular characteristic, opinion, or interest.
The presence of sampling bias distorts research findings and makes it difficult to draw valid conclusions about the entire population. To minimize sampling bias, researchers strive for random sampling methods, which help ensure every member of a population has an equal chance of being selected.