Primers • Data Sampling
 Overview
 Application of Sampling in NLP (Natural Language Processing)
 Random Sampling
 Stratified Sampling
 Diving Deeper: Hard Sampling vs. Negative Sampling
 Hard Sampling (or Hard Negative Mining)
 Negative Sampling
 Citation
Overview
 Sampling techniques are foundational methods employed to derive a representative fraction (the sample) from a larger set (the population). The objective is to draw conclusions about the entire population based on the characteristics of the chosen sample. The ideal sampling technique hinges upon the nature of the population and the research’s goal. Below is an indepth exploration of prevalent sampling techniques:

Random Sampling: At its core, random sampling exemplifies true probability sampling. In this technique, every population member stands an equal likelihood of being a part of the sample, ensuring unbiased representation.

Systematic Sampling: Systematic sampling revolves around a systematic approach where units are selected at uniform intervals from an organized population list. The interval, known as the “sampling interval”, is derived by dividing the population size by the desired sample size. An initial point is chosen randomly, and subsequent selections are made at the determined interval.

Stratified Sampling: This method necessitates the division of the population into analogous subgroups, termed ‘strata’. Each stratum is created based on a particular attribute or characteristic. Samples are then drawn from each stratum, either proportionately or uniformly, ensuring a comprehensive representation of all segments of the population.

Cluster Sampling: Here, the population is segmented into multiple clusters, usually based on geographical or organizational divisions. Instead of sampling from each cluster, a random assortment of clusters is chosen, and all entities within these chosen clusters form the sample.

Multistage Sampling: This sophisticated method blends multiple sampling techniques at successive stages. An exemplar scenario might involve first utilizing cluster sampling to choose particular areas within a city and then employing random sampling within those areas to select households.

Quota Sampling: A nonprobability method, quota sampling ensures that the sample mirrors certain attributes or characteristics of the entire population. Researchers set quotas for individual subgroups, and once those quotas are attained, sampling concludes.

Convenience Sampling: As the nomenclature suggests, this nonprobability method depends on the ready availability of members from the population. While convenient, it might not always yield a representative sample.

Snowball Sampling: Predominantly used for studies involving hardtoreach populations, this method depends on existing participants to refer or recruit subsequent participants. It’s particularly useful for studying networks or specific communities.
 Each method carries distinct advantages and potential limitations. The technique’s selection is integral to the accuracy and reliability of research findings, and it is contingent on the study’s objectives, the population’s structure, and available resources.
Application of Sampling in NLP (Natural Language Processing)
Sampling plays a pivotal role in diverse Natural Language Processing (NLP) tasks. Here are enhanced insights into its applications:

Dataset Creation: Crafting a dataset tailored for specific NLP tasks might not always warrant the use of all extant data. Sampling helps cull a representative fraction that mirrors the broader data spectrum.

Addressing Imbalanced Classes: Text classification tasks occasionally grapple with stark class imbalances. Sampling can rectify this, with undersampling curtailing majority class instances or oversampling amplifying minority class instances.

Negative Sampling in Word Embeddings: In tasks such as word2vec, negative sampling is indispensable. The goal here is to sample negative instances (contextword pairs absent in the text) to enrich the learning experience of the model.

Training Efficacy: At times, computational constraints might preclude the use of extensive data for model training. Here, sampling proves instrumental in choosing a data subset without compromising training quality.

Model Evaluation: Posttraining, models undergo rigorous evaluations. Sampling is often employed to draw a subset of the data explicitly for testing and validation purposes.
Random Sampling
 Random sampling, particularly simple random sampling, does not inherently ensure that the original distribution of the population is reflected in the sample. Unlike stratified sampling, which deliberately segments the population to reflect its diversity, simple random sampling is based on pure chance. Here’s why simple random sampling might not always reflect the original distribution:
 Chance Variability: In simple random sampling, every member of the population has an equal chance of being selected. However, this randomness can lead to samples that are not representative, especially in smaller samples. By chance, the sample might overrepresent or underrepresent certain groups within the population.
 Lack of Stratification: Simple random sampling does not take into account the various subgroups or strata that might exist within a population. If the population is heterogeneous (diverse), the sample may not capture this diversity accurately, especially if the sample size is not large enough.
 Sample Size Matters: The accuracy of simple random sampling in reflecting the population distribution improves with larger sample sizes. In smaller samples, the randomness can lead to greater variability and a higher chance of a nonrepresentative sample.
 Potential Bias: If the method of selecting the sample is not truly random (e.g., using a flawed randomization process), there can be biases in the sample that do not accurately reflect the population.
 In contrast, stratified sampling is designed to ensure that all significant subgroups of the population are adequately represented in the sample, thereby better reflecting the original distribution. Random sampling, while useful and easy to implement, may require a larger sample size or additional sampling techniques to achieve a similar level of representativeness.
Stratified Sampling
 Stratified sampling is a technique used to ensure that the sample more accurately reflects the population from which it is drawn, especially when there are significant differences within the population. Here’s how it helps in maintaining the original distribution:
 Division into Strata: The population is divided into different subgroups or strata that are distinct and nonoverlapping. These strata are based on specific characteristics relevant to the research, such as age, income, education, etc.
 Proportional Representation: In each stratum, elements are chosen based on a random sampling method. The key is that the proportion of each stratum in the sample should reflect the proportion of that stratum in the entire population. This ensures that each subgroup is adequately represented in the sample, preserving the original distribution of the population.
 Combining Strata Samples: After sampling from each stratum, the results are combined to form a single sample. This aggregate sample is more representative of the population than a simple random sample would be, especially in cases where certain strata may be underrepresented.
 Stratified sampling can be done with or without replacement:
 Sampling Without Replacement: This is the most common approach. Once an individual or element is selected from a stratum, it is not replaced, meaning it cannot be chosen again. This approach ensures that each member of the population has an equal chance of being selected.
 Sampling With Replacement: In this method, each member of the population can be selected more than once. This is less common in stratified sampling but may be used in certain situations, like when the population size is very small or when a higher degree of randomness is required.
 The choice between sampling with or without replacement depends on the specific goals and constraints of the research. Sampling without replacement is typically preferred to avoid the possibility of the same individual or element being chosen multiple times, which could skew the results.
Diving Deeper: Hard Sampling vs. Negative Sampling
 Hard Sampling (or Hard Negative Mining): Primarily relevant in object detection tasks, hard negative mining is a nuanced strategy focusing on the most formidable negative examples. These “hard negatives” are instances the model erroneously classifies. By integrating these instances into the training fold, the model is better equipped to discern genuine object instances from mere background interference. This meticulous focus on challenging examples bolsters the model’s feature discernment capabilities.
 Negative Sampling: Predominantly seen in tasks like word2vec and recommendation systems, negative sampling addresses the challenge of a grossly imbalanced dataset. Instead of leveraging every negative example during training, a random subset is periodically selected. This streamlined approach not only diminishes computational demands but often results in models with competitive performance metrics.
 In essence, while both strategies address negative examples during training, hard sampling zeroes in on the most challenging instances. In contrast, negative sampling picks a random subset to optimize computational efficiency. Both techniques are paramount in certain machine learning tasks, optimizing the balance between computational demands and model efficacy.
Hard Sampling (or Hard Negative Mining)
 In the context of object detection tasks, hard negative mining refers to the process of selecting the most challenging negative examples (background patches in an image that do not contain the object of interest) to include in the training set. These challenging negatives, also called “hard negatives”, are the ones that the model currently misclassifies, meaning the model mistakenly predicts them to contain the object of interest.
 Incorporating these hard negatives in the training process helps the model improve its ability to distinguish between true object instances and background noise. The idea is that by focusing on the most challenging examples, the model learns more robust and discriminative features.
Negative Sampling
 This is a strategy often used in tasks where the dataset is extremely imbalanced, like word2vec, recommendation systems, or any scenario where there are vastly more negative examples than positive ones. Negative sampling is a technique where, instead of using all the negative examples in the training process, a small random sample of the negatives is selected and used in each training step.

Negative sampling is very useful in reducing the computational burden of dealing with a large number of negative examples. It can lead to a faster and more efficient training process, and despite its simplicity, it often leads to models with competitive performance.
 So, both hard and negative sampling are strategies to manage the negative examples in your training process. The key difference is that hard sampling focuses on selecting the most challenging negatives, whereas negative sampling involves randomly selecting a subset of negatives for computational efficiency.
 Both hard and negative sampling are techniques used in training machine learning models, especially in tasks like word embedding learning and object detection. They are strategies to sample training instances that can make the learning process more effective and efficient. Let’s break them down:
Citation
If you found our work useful, please cite it as:
@article{Chadha2020DistilledDataSampling,
title = {Data Sampling},
author = {Chadha, Aman and Jain, Vinija},
journal = {Distilled AI},
year = {2020},
note = {\url{https://aman.ai}}
}