Random measurement errors of the same physical quantity if small, should over time cancel, while systemic measurement errors will not. Reading an instrument may produce random errors. If the same person reads it, there is a chance of systemic errors, so having separate individuals make independent readings is one way of reducing systemic error. Errors in calibration of equipment produces systemic errors. Sometime minor flucuations in environment causes highly sensitive equipment to generate random errors. However, using an instrument in an environment that is outside its working range can cause systemic errors.
Bias is systematic error. Random error is not.
Random errors - Random errors can be evaluated through statistical analysis and can be reduced by averaging over a large number of observations. Systematic errors - Systematic errors are difficult to detect and cannot be analyzed statistically, because all of the data is off in the same direction (either to high or too low). Spotting and correcting for systematic error takes a lot of care.
Parallax errors occur due to the shift in position when viewing an object from different angles. Since this shift is constant and predictable, it is considered a systematic error that can be accounted for and corrected in measurements. Systematic errors also affect all measurements in a consistent manner, making them different from random errors.
Sampling error leads to random error. Sampling bias leads to systematic error.
how to reduce the problem of random error and systematic error while doing an experiment
Standard error is random error, represented by a standard deviation. Sampling error is systematic error, represented by a bias in the mean.
systematic errors
Systemic or precisely Systematic Error in a reading taken by an instrument occurs due to the parts installed in it. Random error occurs when we get a number of repetitive readings during the same experiment because of human error. Perfect example for random is "Parallax Method".
Such an error is a recurring error because of a faulty measuring instrument or some recurring experimental condition that distorts the data every time a measurement is made.
You can overcome or reduce the problem of random error and systematic error while doing an experiment by increasing the sample size, which means averaging over a huge number of observations.
The polarity of a random error refers to whether the error is positive or negative relative to the true value. In statistical analysis, random errors can be equally likely to be positive or negative, and their effect should cancel out when many measurements are averaged. Monitoring polarity can help identify biases or systematic errors in data collection or measurement processes.
It must be either, otherwise it is systematic error or bias.