Sampling bias is the non-random selection of samples from a stochastic variable that are used to estimate its distribution that do not accurately reflect the true distribution. Let’s take a specific instance where we might want to use an opinion poll to forecast the results of a presidential election. If our sample of 1000 voters is representative of the electorate as a whole, asking them about their voting intentions can provide a fairly accurate prediction of the likely winner (i e. unbiased). The views of many significant segments of the electorate as a whole (ethnic minorities, elderly people, and blue-collar workers) are likely to be underrepresented in the sample if we only poll the opinions of 1000 white middle-class college students, which lowers our ability to predict the outcome of the election from that sample.

Differences between units from a population sampled and the entire population they represent in an unbiased sample, or between samples taken from a random variable and its true distribution, should only be due to chance. There is a sampling bias if the differences between them are not just the result of chance. When a variable is systematically under- or over-represented compared to its true distribution, sampling bias frequently results (as in the case of the example above from the opinion poll). Because sampling bias is consistent, the estimate of the sampled probability distribution is systematically distorted. Increased data samples cannot eliminate this distortion; instead, it must be corrected using the right techniques, some of which are covered below. In other words, polling 1000 additional white college students won’t increase the accuracy of our opinion survey, but polling 1000 randomly selected voters would Undoubtedly, a biased sample could impact how probability functionals are measured (e.g. g. Since any statistics calculated from that sample have the potential to be consistently inaccurate (e.g., the variance or the entropy of the distribution),

## Biased and Unbiased Samples

## Types of biased samples

Unintentional factors that encourage certain types of people to participate in the study or researcher error could lead to biased samples. There are a few types of biased samples, including:

## What is a biased sample?

When a study’s sample is systematically more likely to be chosen for the study, it is said to have a biased sample. It describes a circumstance in which a particular group of people is favored by the research sample. Because a biased sample does not accurately represent the population, the validity of a study is threatened. Because some populations are underrepresented in the study, biased samples produce skewed results.

## Probability vs. non-probability biased samples

Biased samples can also be probability or non-probability-based. Here are a few examples of each type:

**Probability biased samples**

When each member of a population has an equal chance of being chosen, probability sampling takes place. Although the risk of a biased sample can be decreased because samples are chosen at random, the risk is still present. If the randomly selected samples don’t represent the population being studied, a biased sample may still exist.

**Non-probability biased samples**

A non-randomized sample is one that is biased against probability. This could include a study in which all participants have consented in advance to take part. As it is unlikely to include an accurate representation of the population, this frequently results in a biased sample.

## Examples of biased samples

Biased samples may occur in a variety of research situations. Here are a few examples of biased samples:

**Probability biased sample example**

A researcher is interested in understanding how high school GPAs correlate with college success. They generate a random number for each student in their grade before selecting the sample from those numbers. Even though the study’s participants were chosen at random, not all college students were given the opportunity to participate.

The sample only includes students within the same grade. Additionally, participation in the study required students to sign up, which may be evidence that high school GPA affects which students want to take part in campus research projects.

**Non-probability biased sample example**

For a first-year project, a researcher wants to investigate college students’ sleeping patterns. They invite other students in their class to participate in the study in exchange for the researcher taking part in their study as well.

Only students who are enrolled in the same class are included in this convenience sample. Since everyone in the study is in their first year of college and may all be studying the same thing, this sample is unlikely to accurately represent how sleep affects all college students.

**Pre-screening biased sample example**

A study seeks to evaluate the impact of weekly cardio exercise on stress levels. Potential participants are invited, and those who meet the requirements—such as being able to attend weekly stress tests—are then invited to private meetings. Only people with free time are chosen for the study, which results in a biased sample of people who may also be less likely to experience high levels of stress.

**Undercoverage biased sample example**

A student intends to assess the impact of socioeconomic status on college graduation rates. All college students are invited to participate in the study, where they are required to disclose the family income. The issue with this sample is that it only accounts for the students at that particular college, so it does not fairly represent all populations. Due to the researcher’s enrollment in a selective Ivy League institution, their sample may not represent all populations.

## How to avoid sampling bias

By taking the following actions, you can prevent sampling bias from affecting your research study:

**1. Set up the perimeters of the study**

Prior to selecting the most appropriate method for selecting a sample population, it is crucial to define the precise parameters of the investigation. This entails the hypothesis you intend to test as well as the data and materials you’ll require for the experiment. Make a list of the independent and dependent variables you plan to examine. The independent variable is the one that is changing, and the dependent variable is the subject of your study.

**2. Identify the target population**

Define your target populations, being as specific as possible. For instance, you should gather a sample of college students from various populations if you’re researching how many hours of sleep affect college grades. Be careful of convenience sampling during this step. When you select a sample based on convenience, you are engaging in convenience sampling.

**3. Determine how best to reach the target population**

Determine the best way to obtain a sample of college students that represents a diverse group of students in terms of gender, race, and culture. Oversampling can be effective in preventing a biased sample. To ensure that everyone is included in the study, participants from specific underrepresented groups must be chosen. You can then change it to reflect the percentage of the population once you have responses from the underrepresented group.

**4. Review survey questions**

It can be beneficial to have a coworker review survey questions and study components in addition to editing and reviewing them yourself. This can assist you in recognizing any potential biases that you might not otherwise be aware of. To prevent the sample from becoming biased, it’s crucial to keep reviewing the study as it goes along.