Convenience sampling, within the context of AI, refers to a non-probabilistic method used frequently when collecting data for training models in situations where time, resources, or access to substantial datasets are limited. This method involves selecting samples based on their ease of obtaining rather than random selection, making it suitable for exploratory analysis and pilot studies. However, it is prone to bias as it may not accurately represent the broader population, potentially leading to overfitting or poor generalization in AI models. Despite these limitations, convenience sampling is often employed in preliminary stages to quickly gather data and test hypotheses before advancing to more rigorous sampling techniques or in cases where obtaining a perfectly random sample is logistically challenging or cost-prohibitive.
Convenience sampling's origins can be traced back to the early 20th century, with its methods gaining attention during the mid-20th century due to the need for pragmatic approaches in social science and marketing research. In AI, this technique became prominent as researchers sought to quickly amass data for training initial model versions, particularly during the early 2000s as rapid advances in computing power made sophisticated model development more accessible.
Key contributors to the articulation and exploration of convenience sampling in AI are less about specific individuals and more about institutions and organizations that have advocated for practical data collection methods. In the academia-jeweled field of AI, such methodologies have been discussed widely in research from statistical societies and educational bodies focusing on data science and AI advancement.