Question

identify and explain with examples the differences between the measurement errors of bias and random error

Answer 1

Measurement Error Bias- Errors-in-variables, or measurement error situation happens when your right hand side variable(s); your in a model is measured with error. If represents the price of a liquid stock, then it is accurately measured because the trading is so frequent. But if is a volatility, well, it is not accurately measured. We simply don’t yet have the power to tame this variable variable.

Ignoring measurement errors leads to biased estimates and, good God, inconsistent estimates.

Example-

Measurement bias results from poorly measuring the outcome you are measuring. For example:

• The survey interviewers asking about deaths were poorly trained and included deaths which occurred before the time period of interest.

• This would lead to an overestimate of the mortality rate because deaths which should not be included are included.

  Random Error- Random errors are statistical fluctuations (in either direction) in the measured data due to the precision limitations of the measurement device. Random errors usually result from the experimenter's inability to take the same measurement in exactly the same way to get the exact same number.

Example- Random errors in experimental measurements are caused by unknown and unpredictable changes in the experiment. ... Examples of causes of random errors are: electronic noise in the circuit of an electrical instrument, irregular changes in the heat loss rate from a solar collector due to changes in the wind.