Probability sampling can be contrasted with non-probability sampling. In non-probability sampling, some elements in the sampling frame have either 0 probability to be selected, or their probability is unknown. As a consequence, we cannot accurately determine the margin of error. It's also impossible to determine the likelihood that a sample is representative of the population. There are several types of non-probability sampling. I'll discuss the four most common types. Convenience sampling, snowball sampling, purposive sampling, and quota sampling. Convenience sampling or accidental sampling is the simplest form of non-probability sampling. In convenience sampling, elements are selected that are the most convenient, the most easily accessible. For example, if I'm interested in investigating the effectiveness of online lectures on study performance, I could recruit students in courses that I teach myself. Of course, this is a highly selective sample of students from a particular university in a particular bachelor program. Results will almost certainly be influenced by specific characteristics of this group, and might very well fail to generalize to all university students in my country, let alone students in other countries. So the risk of bias is high, and we have no way to determine how closely the sample value is likely to approach the population value. Even so, convenience samples are used very often. Because sometimes, it's simply impossible to obtain a sampling frame. In other cases, the effort and expense necessary to obtain a sampling frame are just not worth it. For example, when a universalistic causal hypothesis is investigated. Snowball sampling is a specific type of convenience sampling. In snowball sampling, initially, a small group of participants is recruited. The sample is extended by asking the initial participants to provide contact information for possible new participants. These new participants are also asked to supply contacts. If all participants refer new ones, the initially small sample can grow large very quickly. Suppose we want to sample patients who suffer from a rare type of cancer. We could approach a patient interest group for example, and ask the initial participants if they can put us in contact with other patients that they know through other interest groups, or through their hospital visits. We continue to ask new participants to refer others to us until the required sample size is reached. Snowball sampling is very useful for hard to reach, closed community populations. Of course, all disadvantages of convenience sampling also apply to snowball sampling, maybe even more so. Because there's the added risk that we're selecting a clique of friends, colleagues, or acquaintances. These people could share a characteristics that differ systematically from others in the population. In purposive sampling, elements are specifically chosen based on the judgement of the researcher. A purposive sample can consist of elements that are judged to be typical for the population, so that only a few element values are needed to estimate the population value. A purposive sample can consist of only extreme elements. For example, to get an idea of the effectiveness of social workers working with extremely uncooperative problem families. Elements can also be purposively chosen, because they're very much alike, or reversely very different. For example, to get an idea of the range of values in the population. Or, elements can consist of people who are judged to be experts. For example, when research concerns opinions on matters that require special knowledge. Purposive sampling is used mostly in qualitative research, so I won't go into further details here. Suffice it to say that purposive sampling suffers all the same disadvantages that convenience sampling does. The researcher's judgment can even form an additional source of bias. Quota sampling is superficially similar to stratified random sampling. Participants in the sample are distinguished according to characteristics such as gender, age, ethnicity, or educational level. The relative size of each category in the population is obtained from a national statistics institute, for example. This information is used to calculate how many participants are needed in each category, so that the relative category sizes in the sample correspond to the category sizes in the population. But instead of randomly selecting elements from each stratum, participants for each category are selected using convenience sampling. Elements are sampled until the quotas in all categories are met. Although this approach might seem to result in a representative sample, all kinds of biases could be present. Suppose the choice of participants is left to an interviewer, then it's possible that only people who seem friendly and cooperative are selected. If a study uses non-probability sampling, the results should always be interpreted with great caution, and generalized only with very great reservation.