Convenience sampling

See also: Machine learning terms

Introduction

Convenience sampling, also known as opportunity sampling or accidental sampling, is a non-probability sampling method utilized in various fields, including machine learning and statistics. It involves selecting a sample based on its accessibility and ease of collection, rather than following a random sampling process. Despite its limitations, convenience sampling can serve as a useful preliminary step for exploratory research or when resources and time are limited.

Characteristics of Convenience Sampling

Convenience sampling differs from other sampling methods in several ways, primarily in its lack of randomization and the use of readily available data. This section will outline the primary characteristics of convenience sampling.

Accessibility

The most notable feature of convenience sampling is the ease of access to the sample population. This method selects data points that are readily available and can be easily collected, without considering the representativeness of the sample. In machine learning, this may involve selecting a dataset that is easily obtained or using a pre-existing dataset, such as those found in public repositories like UCI Machine Learning Repository or Kaggle.

Non-random Selection

Unlike probability sampling methods, convenience sampling does not rely on randomization. Instead, researchers select the sample based on its accessibility, which may result in a biased or unrepresentative sample. The lack of randomization can lead to skewed results and limit the generalizability of findings.

Low Cost and Speed

A significant advantage of convenience sampling is its low cost and quick implementation. Researchers can collect data rapidly without investing in expensive equipment or spending time on complex sampling procedures. This makes it an attractive option for preliminary studies or situations with limited resources and time constraints.

Limitations of Convenience Sampling

While convenience sampling offers several advantages, it is essential to consider the limitations associated with this method.

Lack of Representativeness

Due to its non-random nature, convenience sampling often results in samples that may not be representative of the overall population. This can lead to biased results and limit the ability to make accurate inferences about the broader population. In machine learning, this may result in poor model performance when applied to new, unseen data.

Sampling Bias

Convenience sampling is prone to sampling bias, as researchers may inadvertently select data points based on their preferences or preconceived notions. This can result in a sample that does not accurately represent the population and may skew the findings of the study.

Explain Like I'm 5 (ELI5)

Imagine you want to know what kind of ice cream kids at a school like best. Convenience sampling would be like asking the kids who are closest to you, maybe those in your class or on your street, instead of choosing kids randomly from the whole school. This can make the results less accurate because you're not asking a good mix of kids from different parts of the school. But, it's easier and faster to ask the kids close to you, so it might be a good starting point before you do a more detailed survey.