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# Bias|Definition & Meaning

## Definition

**Bias** is a statistical phrase that **represents** a systematic **variation** from the real value. It is a sampling **procedure** that may show deep **issues** for the researcher as a mere addition **cannot** decrease the **sample** size. Bias is the **difference** between the desired **value** and the actual value of the **parameter.**

**Bias** is a word in statistics, that describes the **direction** of the measurement procedure. It **means** that it assesses the over or **devaluation** of the value of the population parameter. Consider an **instance,** you have the formula to estimate the mean of the population and you manage to find the **measurement** using the formula, which is the true **examination** of the population. Now, by utilizing the biased **estimator,** it is straightforward to see the **difference** between the **actual** value and the statistically **predicted** value of the population **parameter. **

There are many types of **Bias** in Statistics and are **classified** into two parts, that are The **Measurement** Bias and The **Non-Representative** Sampling Bias. Below we will discuss the **classification** of bias and its various kinds.

**Understanding Bias**

Bias in statistics is the **discrepancy** between the **expected** value of an **estimator** and its **estimated** value. **Figure 1** for instance has a **population** mean value at **point G**. That means the **expected** value of the **estimator** is at **point G**. Now you take a **sample** from the **population** and compute its **mean** value and find out that your **estimator** has estimated the sample mean at** point H**. Actual value is **G** but your estimator has **estimated H**. so the difference **between** the **actual** value G and the **estimated** value H is bias. In simple terms, **Bias** is the deviation from the **mean** value.

Think of **archery** and consider that your bow is **sighted inaccurately.** High bias does not indicate **you’re** hitting all over the area but may **provoke** an excellent archer to **strike** below the **bullseye** all the spell. As in the **below** figure, the archer’s hits were **accurate** but because of **high** bias, he could not **connect** the **yellow** region.

The below **figure** shows the **sampling** bias which refers to a **biased** sample induced by **non-random** sampling. To show an **example,** suppose that there are seven people in a **chamber** and you question if they like **apples** or **bananas.** If you only observe the three females and **infer** that most people like apples, you’d have **demonstrated** sampling bias because you **have** not observed any male **preference.**

**Measurement Bias**

**Measurement** bias takes place for the whole **duration** when holding out a survey, and the causes for its impacts **because** of the following;

**The Error Takes Place Solely When Recording Data.**

When registering any data, we **obtain** errors because of the **malfunction** of the tools used for reading data, or, also due to the weak **handling** of the instruments by the data collection **individuals.**

**Leading Questions for the survey.**

Preparations of the **questioning** that are needed for the survey might be **placed** in a way that is **interviewer-friendly,** responses will be according to the **interests** of the **interviewer,** and queries that will be responded to which are **desired** by the interviewer/researcher. There must be more **preferences** for them to get a decent report.

**False Responses from Respondents.**

**Circumstances** can arise when **numerous** responders misinterpret the **questions** and provide an incorrect answers. In the care of **older** respondents, when they are **envisioned** to fill the survey responses by **remembering** their earlier **experiences,** this may cause further **misconception** and this could bring **incorrect** inputs due to weakly record **keeping**

**Non-Representative Bias**

This **happens** when a survey model represents the **population** incorrectly, which is due to operating **involuntarily** with only an exact split of the **population,** and here the representative **becomes** unrepresentative of the entire population. The main types of **selection** bias are:

**Undercoverage** Bias happens when some **respondents** of the instance population are not **wholly** illustrated. The **reasoning** behind such a bias is the **comfort** of sampling, that takes place **when** the information is **collected** from an easily **available** source. An **instance** can be the local **supermarket.**

**Non-response** Bias happens when the **people** who are identified to **designate** a survey are **reluctant** or unable to take **place** in the survey. In this matter, the **respondents** have an upper hand over the **survey’s** outcome.

**Voluntary** response bias **happens** when members who **bring** samples are **self-selected** volunteers. These **Answers** give a **faulty** and wrong **illustration** of the general population who are in **acceptance** of strong **opinions.**

**Survivorship** Bias directs to that type of survey that calls for the **survival** of a long procedure for being **measured** as a complete response that offers a rise in **biased sampling.**

**Bias as an Estimator**

The **bias** of an estimator (bias function) in statistics, is the **distinction** between this estimator’s **predicted** value and the **true** value of the parameter being evaluated. The **unbiased** estimator is an estimator for **finding** rules with zero bias. In statistics, bias is an **accurate** property of an **estimator.** Bias is a separate idea from **consistency.** Uniform estimators join in **probability** to the actual value of the **parameter** but may be **unbiased** or **biased.**

All others **being** equal, an unbiased **estimator** is preferable over a **biased** estimator, **although,** in reality, biased **estimators** with slight biases are often used. When a **biased** estimator is utilized, the bounds of the **bias** are computed. A **biased** estimator may be **operated** for various **causes,** or because an unbiased estimator **doesn’t** exist without **additional** inferences about a **population** or because an estimator is **complex** to estimate, or **because** a biased **estimator** might be unbiased with **regard** to different **measurements** of the main tendency, or because a biased **estimator** offers a **lower** value of little **loss** function resembled **with** unbiased **estimators** or because in some **scenarios** being unbiased is too robust a **condition,** and the singular unbiased estimators are not **applicable.**

**Solving Problem – Bias**

### Example 1

Source of **bias** in which the population chooses to answer and **commonly** only people with very firm **opinions** answer.

A. Response Bias

B. Undercoverage

C. Convenience Sampling

D. Voluntary Response

### Solution

Answer:

**D. Voluntary Response**

### Example 2

Source of **bias** that arises when you request **people** who are **Comfortable** to **reach**

A. Response Bias

B. Convenience Sampling

C. Nonresponse

D. Voluntary Response

### Solution

Answer:

**B. Convenience Sampling**

*All images/graphs are created using GeoGebra.*