"Bias is an inclination towards something, or a predisposition, partiality, prejudice, preference, or predilection. Bias may also refer to: In science and statistics: 1. Bias (statistics), the systematic distortion of a statistic - A biased sample is one that is falsely taken to be typical of a population from which it is drawn - Estimator bias describes an estimator whose expectation differs from the true value of the parameter being estimated 2. Cultural bias, interpreting and judging phenomena in terms particular to one's own culture 3. Cognitive bias, any of a wide range of observer effects identified in cognitive science - Confirmation bias, the tendency to interpret information in a way that confirms preconceptions - Perception bias, also known as cognitive perspective, the choice of a context or a reference 4. Infrastructure bias, the influence of existing social or scientific infrastructure on scientific observations 5. Notation bias, a form of cultural bias in which a notation induces the appearance of a nonexistent natural law 6. Publication bias bias towards publication of certain experimental results." Source and further information: http://en.wikipedia.org/wiki/Bias_(disambiguation%29 Further information: http://en.wikipedia.org/wiki/Bias

In school math, the word biased is often applied to the word die or dice. biased dice do not give out each number with equal probability. In statistics an unbiased estimate is one where the average of a large number of similar estimates would get you as close as you like to the true value. An example of a biased estimate would be to estimate the standard deviation of a population by using the standard deviation of a sample of size n To see this is wrong, try n = 1. The standard deviation of a sample of 1 is always zero - there is no deviation in the single value. It doesn't make any difference how many samples of size 1 you take, the estimate is always zero and the average is zero. But the population probably does have some deviation, so the estimate is biased.