This website is using cookies to ensure you get the best experience possible on our website.
More info: Privacy & Cookies, Imprint
The concept of BIAS in estimation refers to a systematic deviation of the estimated values from the actual values. It occurs when the estimation procedure contains systematic errors or assumptions that skew the results.
There are several ways to reduce the BIAS in the estimate:
Selection of a suitable estimation method: Choosing the right estimation method is important in order to minimize the BIAS. Different methods have different properties and assumptions that can lead to bias. Therefore, the most appropriate estimation method should be selected for the specific use case.
Considering sample bias: A sample may be biased if it is not representative of the entire population. To reduce BIAS, care should be taken to use a random and representative sample. This can be achieved by appropriate sampling methods.
Checking of model assumptions: Estimation methods are often based on certain assumptions about the distribution of the data. Failure to meet these assumptions can lead to bias. It is important to review the model assumptions and make appropriate adjustments to reduce BIAS.
Using larger samples: A larger sample can help reduce BIAS by providing a better estimate of the actual parameters. With larger samples, the estimate usually approaches the true value.
Sensitivity analysis: A sensitivity analysis can help evaluate the influence of different assumptions or parameters on the estimate. By varying assumptions or parameters, the BIAS can be identified and minimized.
Avoiding selection bias: Selection bias occurs when certain data points or observations are omitted due to bias in selection or inclusion criteria. It is important to recognize the possibility of selection bias and take steps to avoid it.
It should be noted that BIAS cannot always be completely eliminated as in some cases it may be based on inherent limitations or limited information. However, reducing the BIAS is an important goal in the estimation in order to achieve the most accurate and reliable results possible.