# What does bias mean?

Related Calculator: Mean Bias Error

Related Calculator: Relative bias in percent

Bias is a word you face with it many of time in statistics, forecasting a value, and etc., and you probably know that it means something not good. But "What does bias mean?" Bias refers to the tendency of a measurement process to over- or under-estimate the value of a population parameter. In survey sampling, for example, bias would be the tendency of a sample statistic to systematically over- or under-estimate a population parameter (Imdad Ullah, 2012). Bias can seep into your results for a slew of reasons including sampling or measurement errors, or unrepresentative samples.

Bias is the error in estimates due to systematic mistakes that lead to consistently high or low results as compared to the actual values. When estimates are biased they are consistently wrong in one direction due to mistakes in the system used for the estimates. For example, a weather prediction may consistently forecast rainfalls that are higher than those actually observed. The forecast is biased, and somewhere in the system there is a mistake that gives too high an estimate. If the forecast method is unbiased, it may still predict rainfalls that are not correct, but the incorrect rainfalls will sometimes be higher and sometimes lower than the rainfalls observed.

The individual bias of an estimate known to be biased is the difference between the estimated and actual values. If the estimate is not known to be biased, the difference could also be due to random error or other inaccuracies. Contrary to bias, which always operates in one direction, these errors can be negative or positive. To compute the bias of a method used for many estimates, find the errors by subtracting each estimate from the observed value. Summation all the errors and divide by the number of estimates to achieve the bias. If the errors add up to zero, the estimates were unbiased, and the method delivers unbiased outputs. If the estimates are biased, it may be possible to find the source of the bias, and delete it to amend the method.

It's crucial to determine potential sources of bias when planning a sample survey. When we say there's potential bias, we should also be able to argue if the results will probably be an overestimate or an underestimate. Try to recognize the source of bias, and contemplate on the direction of the bias. It is mean you should consider the amount of overestimate or underestimate. Bias can occur in any of a number of ways:

• In the way the sample is selected.
• In the way data are collected.

For calculating the amount of simple bias, you can use the following equation:

Bais = XObs - XGen

In this equation XObs states to the observation or actual data, and XGen to the generation or modeled (estimation) data. Pay attention to the scale of data and the bias value's scale. For example, if you want to calculate yearly bias of precipitation data, you should calculate the bias of every 12 months and then get average of 12 values for the yearly bias amount.

In statistical hypothesis testing, a test is said to be unbiased if, for some alpha level (between 0 and 1), the probability the null is rejected is less than or equal to the alpha level for the entire parameter space defined by the null hypothesis, while the probability the null is rejected is greater than or equal to the alpha level for the entire parameter space defined by the alternative hypothesis (Neyman, Pearso, 1936).

Note: If the statistic is unbiased, the average of all statistics from all samples will average the true population parameter. There are different types of bias in various field of sciences as Omitted-variable bias, Selection bias, Spectrum bias, Funding bias, Exclusion bias, Attrition bias, Recall bias, Central Tendency Bias, Assignment Bias, Performance Bias, Referral Bias, Aggregation Bias, Ascertainment Bias, Assignment Bias, and different other types.

References of 'What does bias mean':
1. Neyman, J; Pearson, E S (1936). "Contributions to the theory of testing statistical hypotheses". Stat. Res. Mem. 1: 1-37.
2. Imdad Ullah, Muhammad, 2012. "Bias: The difference between the Expected Value and True Value". Basic Statistics and Data Analysis.
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