---
title: "Sampling (statistics)"
chunk: 6/8
source: "https://en.wikipedia.org/wiki/Sampling_(statistics)"
category: "reference"
tags: "science, encyclopedia"
date_saved: "2026-05-05T03:17:18.009917+00:00"
instance: "kb-cron"
---

Accidental sampling (sometimes known as grab, convenience, or opportunity sampling) is a type of nonprobability sampling which involves the sample being drawn from that part of the population which is close to hand. That is, a population is selected because it is readily available and convenient. It may be through meeting the person or including a person in the sample when one meets them or chosen by finding them through technological means such as the internet or through phone. The researcher using such a sample cannot scientifically make generalizations about the total population from this sample because it would not be representative enough. For example, if the interviewer were to conduct such a survey at a shopping center early in the morning on a given day, the people that they could interview would be limited to those given there at that given time, which would not represent the views of other members of society in such an area, if the survey were to be conducted at different times of day and several times per week. This type of sampling is most useful for pilot testing. Several important considerations for researchers using convenience samples include:

Are there controls within the research design or experiment which can serve to lessen the impact of a non-random convenience sample, thereby ensuring the results will be more representative of the population?
Is there good reason to believe that a particular convenience sample would or should respond or behave differently than a random sample from the same population?
Is the question being asked by the research one that can adequately be answered using a convenience sample?
In social science research, snowball sampling is a similar technique, where existing study subjects are used to recruit more subjects into the sample. Some variants of snowball sampling, such as respondent driven sampling, allow calculation of selection probabilities and are probability sampling methods under certain conditions.

=== Voluntary sampling ===

The voluntary sampling method is a type of nonprobability sampling. Volunteers choose to complete a survey.
Volunteers may be invited through advertisements in social media. The target population for advertisements can be selected by characteristics like location, age, sex, income, occupation, education, or interests using tools provided by the social medium. The advertisement may include a message about the research and link to a survey. After following the link and completing the survey, the volunteer submits the data to be included in the sample population. This method can reach a global population but is limited by the campaign budget. Volunteers outside the invited population may also be included in the sample.
It is difficult to make generalizations from this sample because it may not represent the total population. Often, volunteers have a strong interest in the main topic of the survey.

=== Line-intercept sampling ===

Line-intercept sampling is a method of sampling elements in a region whereby an element is sampled if a chosen line segment, called a "transect", intersects the element.

=== Panel sampling ===
Panel sampling is the method of first selecting a group of participants through a random sampling method and then asking that group for (potentially the same) information several times over a period of time. Therefore, each participant is interviewed at two or more time points; each period of data collection is called a "wave". The method was developed by sociologist Paul Lazarsfeld in 1938 as a means of studying political campaigns. This longitudinal sampling-method allows estimates of changes in the population, for example with regard to chronic illness to job stress to weekly food expenditures. Panel sampling can also be used to inform researchers about within-person health changes due to age or to help explain changes in continuous dependent variables such as spousal interaction. There have been several proposed methods of analyzing panel data, including MANOVA, growth curves, and structural equation modeling with lagged effects.

=== Snowball sampling ===

Snowball sampling involves finding a small group of initial respondents and using them to recruit more respondents. It is particularly useful in cases where the population is hidden or difficult to enumerate.

=== Theoretical sampling ===

Theoretical sampling occurs when samples are selected on the basis of the results of the data collected so far with a goal of developing a deeper understanding of the area or develop theories. An initial, general sample is first collected with the goal of investigating general trends, where further sampling may consist of extreme or very specific cases might be selected in order to maximize the likelihood a phenomenon will actually be observable.

=== Active sampling ===
In active sampling, the samples which are used for training a machine learning algorithm are actively selected, also compare active learning (machine learning).

=== Judgmental selection ===
Judgement sampling, also known as expert or purposive sampling, is a type of non-random sampling where samples are selected based on the opinion of an expert, who can select participants based on how valuable the information they provide is.

=== Haphazard sampling ===

Haphazard sampling refers to the idea of using human judgement to simulate randomness. Despite samples being hand-picked, the goal is to ensure that no conscious bias exists within the choice of samples, but often fails due to selection bias. Haphazard sampling is generally opted for due to its convenience, when the tools or capacity to perform other sampling methods may not exist.
The major weakness of such samples is that they often do not represent the characteristics of the entire population, but just a segment of the population. Because of this unbalanced representation, results from haphazard sampling are often biased.

== Replacement of selected units ==