Biase

Bias

If the sampling distribution is known then the ability of the sample statistic to estimate the corresponding population parameter can be determined.
In particular, the sampling distribution determines the expected value and variance of the sampling statistic. If the expected value of the statistic is equal to the population parameter, the estimator is unbiased. If the variance of the statistic is 'small' and it is also unbiased then an observed statistic is likely to be close to the population parameter.
Bias = distance between parameter and expected value of sample statistics
Subsequently, sample statistics can be classified as shown in the following diagrams.

1. Estimates have low bias because their average is near the population parameter, but have high variability because they are widely spread and a single sample value could be far from the parameter.
2. Estimates have bias because the expected value is not equal to the parameter.
   They also have high variability because they are widely spread out.
3. In this case the estimates are biased because all of them are systematically higher than the population parameter
   The sample statistics have, however, low variability because they are all close together.
4. 

In this case the estimates have both low bias and low variability. Experimental design aims to simultaneously reduce bias and variability by producing a sampling distribution as shown in 4.
In general
sample statistic = population parameter + bias + chance variation
Inferences about the characteristics of a population are based on data from a sample.

• If the sample is not representative of the population being studied, the sample statistic may be biased so you cannot use it to make valid inferences about the population parameter
• To minimise bias the sample should be chosen by random sampling from a list of all individuals in the relevant population. This list is called the sampling frame. It is essential.
• For a simple random sample the individuals are chosen in such a way that each individual in the sampling frame has an equal chance of being selected. This may involve using computer generated random numbers to select the sample.

Reference 

https://surfstat.anu.edu.au/surfstat-home/4-1-1.html