In statistical tests there are 2 main types of Errors, Type I and Type II. Type 1 errors occur when you reject a null hypothesis that is actually true and is thus refereed to as a false positive. Type II errors are essentially the opposite, accepting a null hypothesis that is false, and is often called a false negative. You can reduce the risk of a type I error by lowering the value of P that you're significance test must return to reject the null, but doing so will increase the chance of a type II error. The only way to reduce both is to increase the entire sample size. Alternatively, in some cases, it may also be possible to lower the standard deviation of the experiment, which would also decrease the risk of type I and type II errors.
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.
Increase n or sample size.
A biased error is one that is caused by a factor inherent to the source of the error. An unbiased error is one that comes from anywhere.
use proper instruements. make sure to follow care and use
Accept lower p-values (meaning lower in magnitude; values tending toward zero).--And don't forget that by reducing the probability of getting a type I error, you increase the probability of getting a type II error (inverse relationship).
The significance level can be reduced.
In any experimental test there are factors that can contribute to error. For example in a biochemical test if you add the compounds with a faulty pippete then the amount of reagent per sample would vary and that would contribute to a noisy measurement. So by controlling experimental conditions as best as one is able error can be reduced. Never completely eliminated, but managed.
The larger the sample, the lower the % error.. so to reduce a % error, increase your sample size.
SEE; poke-yoke (error proofing) process capability statistical process control
With probability sampling you have no control over the units that are sampled. So the only way to reduce the margin of error is to increase the size of the sample.
Systematic error is the result of complete equilibrium. The method to reduce systematic error is to introduce a proof that demonstrates the group has error in their consensus.
3.a Explain Stop-and-Wait Error Control and Sliding Window Error Control techniques.
how to reduce the problem of random error and systematic error while doing an experiment
take reading 4 to 5 times, and take its average this will reduce error
Data link error control checks for error in each router and end-system, but transport layer error control checks for error only at the end-systems.
It should reduce the sample error.
Increase sample size.