Bias: an extraneous latent influence on, unrecognized conflated variable in, or selectivity in a sample which influences its distribution and so renders it unable to reflect the desired population parameters.

(Personal Definition) Bias: a limited view of something that doesn't account for all possibilities and can make decisions/ results look one sided.

Types of Bias in Statistics:

Undercoverage Bias: occurs when some members of the population are inadequately represented in the sample.

Voluntary Response Bias: occurs when sample members are self- selected volunteers, like in a voluntary sample.

Nonresponse Bias: Sometimes, individuals chosen for the sample are unwilling or unable to participate in the survey. Results in a low response rate.

(For more examples visit the second link provided)

"Bias:" "Bias is a general statistical term meaning a systematic (not random) deviation from the true value. A bias of a measurement or a sampling procedure may pose a more serious problem for researcher than random errors because it cannot be reduced by mere increase in sample size and averaging the outcomes. For example, if electronic scales systematically increase the real weight by, say 0.1% (besides the random variation in both directions from the true weight), then the averaging over 1,000 or 1,000,000 measurements still has the same bias - 0.1% higher than the true weight. Another example is an opinion poll focused on presidential candidate preferences among the population of New York City. A random sample of 1000 individuals has been drawn at random from households in the borough of Queens. If personal preferences vary from borough to borough within NYC, then such a poll is biased. Even if we increase the sample size up to 100,000, the systematic error is still the same. (The deviation is equal to the difference between the population of this region and the whole city population.)"

I chose this definition of Bias because it explained the general and the meaning of measurement Bias and how it affects Data Collection. It also included Examples of How Biases work in Statistics.

Bias - by (Labid Khandaker)

Here is a video that better explain Biases in Statistics.

## CHAPTER 1.5 - Bias

Bias: an extraneous latent influence on, unrecognized conflated variable in, or selectivity in a sample which influences its distribution and so renders it unable to reflect the desired population parameters.

(Personal Definition) Bias: a limited view of something that doesn't account for all possibilities and can make decisions/ results look one sided.

## Types of Bias in Statistics:

Undercoverage Bias: occurs when some members of the population are inadequately represented in the sample.Voluntary Response Bias: occurs when sample members are self- selected volunteers, like in a voluntary sample.

(For more examples visit the second link provided)Nonresponse Bias: Sometimes, individuals chosen for the sample are unwilling or unable to participate in the survey. Results in a low response rate.(Definition for bias: http://dictionary.reference.com/browse/bias?s=t )

Bias Examples: http://stattrek.com/survey-research/survey-bias.aspx

Bias - by (Labid Khandaker)"Bias:""Bias is a general statistical term meaning a systematic (not random) deviation from the true value. A bias of a measurement or a sampling procedure may pose a more serious problem for researcher than random errors because it cannot be reduced by mere increase in sample size and averaging the outcomes.

For example, if electronic scales systematically increase the real weight by, say 0.1% (besides the random variation in both directions from the true weight), then the averaging over 1,000 or 1,000,000 measurements still has the same bias - 0.1% higher than the true weight.

Another example is an opinion poll focused on presidential candidate preferences among the population of New York City. A random sample of 1000 individuals has been drawn at random from households in the borough of Queens. If personal preferences vary from borough to borough within NYC, then such a poll is biased. Even if we increase the sample size up to 100,000, the systematic error is still the same. (The deviation is equal to the difference between the population of this region and the whole city population.)"

Source: http://www.statistics.com/index.php?page=glossary&term_id=717

I chose this definition of Bias because it explained the general and the meaning of measurement Bias and how it affects Data Collection. It also included Examples of How Biases work in Statistics.

Bias - by (Labid Khandaker)## Here is a video that better explain Biases in Statistics.

Source: http://youtu.be/5K1Hg-pSY1A

<iframe width="420" height="315" src="//www.youtube.com/embed/5K1Hg-pSY1A" frameborder="0" allowfullscreen></iframe>

(Labid Khandaker LK)