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Non-probability sampling is a method where the probability of selecting any particular member from the target population is unknown and not quantifiable. Unlike probability-based approaches, it is cost-effective and time-efficient but presents challenges such as bias and limited relevance to broader populations. This makes it useful when resources are constrained or when studying specific populations.

There are four common types of non-probability sampling: convenience, judgment, quota, and snowball sampling. Convenience sampling chooses easily accessible participants, such as surveying mall shoppers. Judgmental sampling involves researchers selecting participants using specific criteria. Quota sampling ensures diverse population representation by selecting customers based on predetermined quotas. Snowball sampling allows existing participants to recruit new participants.

Convenience sampling involves choosing participants based on their accessibility and ease of reach. For example, a study on dietary habits might survey people at a local gym, focusing on readily available people. This method is quick and inexpensive but may not represent the broader population accurately, as it captures only those who are easily accessible.

Judgmental or purposive sampling involves selecting participants based on specific characteristics or expertise relevant to the research. For example, experienced marathon runners would be chosen to evaluate the effectiveness of a new sports drink. Companies also use this method by selecting customers with particular purchase histories to understand preferences and behaviors.

Quota sampling ensures that specific subgroups are represented in the sample according to predetermined quotas. For instance, in a study on urban transportation preferences, researchers might ensure their sample includes a certain percentage of cyclists, drivers, and public transit users. This approach helps reflect population distribution and ensures diverse representation without random selection.

Snowball sampling relies on initial participants to recruit others from their networks, making it particularly useful for studying hard-to-reach or specialized populations. For example, initial gamers refer others in their networks, providing insights into preferences and behaviors of niche communities. This method is effective for underground music scenes and other difficult-to-access groups.

Non-probability sampling can introduce selection, response, and measurement biases that limit how well results apply to a broader population. Selection bias occurs when certain groups are systematically excluded or overrepresented. Response bias happens when participants answer differently than the general population. These limitations mean findings may not generalize beyond the specific sample studied.