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Large samples can be just as biased as small samples, depending upon how they are selected. For example, you want to do a survey to see how popular the President is, but you only interview men, refusing to interview women. No matter how many men you interview, this bias still exists. A sample of a million men is still biased if you have excluded women. (Although the data are still significant as long as you recognize that the bias exists.)
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∙ 14y ago
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What is a sample that is taken without bias?
A randomly selected sample.
What is sample taken without bias?
When a p-n junction is taken without a bias, it forms a PHOTO VOLTAIC CELL.
One way to ensure less sample bias?
Representative/random
What is the definition of biased sample for math?
A biased sample is a Statistical Sample in which the sample is biased or have more samples of the things that is being influenced.
When you sample with replacement are the trials independent?
They are, if the sampling and replacement processes don't introduce any bias.
A sample taken without bias?
random sample
What is a sample taken without bias?
Random Sample
Science what is a sample taken without bias?
The answer is Random Sample
What is a sample that is taken without bias?
A randomly selected sample.
What is an unwanted influence on a sample?
bias
How do you ensure less sample bias?
The sample should be selected randomly.
What is a sample taken with out bias?
random or blind
What are the three types of bias that can influence a science experiment?
There are many more types of bias than just three! You can have bias when you specify and select your study sample (such as selecting the wrong sample size or basing your sample on popularity), when you actually perform the experiment (such as contamination or using a bogus control), when you measure the outcomes (such as personal expectations or instrument error) and when you analyze and interpret your data (such as mistaken identity or mistaken significance). Each of these areas has several types of bias associated with it. Here is a good WikiPedia article that lists all of the different types of bias for you.
What are three types of bias that can influence a science experiment?
There are many more types of bias than just three! You can have bias when you specify and select your study sample (such as selecting the wrong sample size or basing your sample on popularity), when you actually perform the experiment (such as contamination or using a bogus control), when you measure the outcomes (such as personal expectations or instrument error) and when you analyze and interpret your data (such as mistaken identity or mistaken significance). Each of these areas has several types of bias associated with it. Here is a good WikiPedia article that lists all of the different types of bias for you.
What is a sample taken without bias a random what?
Selection, choice
How is it that a random samples gives a fairly accurate representation of public opinion?
The main point here is that the Sample Mean can be used to estimate the Population Mean. What I mean by that is that on average, the Sample Mean is a good estimator of the Population Mean. There are two reasons for this, the first is that the Bias of the estimator, in this case the Sample Mean, is zero. A Bias other than zero overestimates or underestimates the Population Mean depending on its value. Bias = Expected value of estimator - mean. This can be expressed as EX(pheta) - mu (pheta) As the Sample Mean has an expected value (what value it expects to take on average) of itself then the greek letter mu which stands for the Sample Mean will provide a Bias of 0. Bias = mu - mu = 0 Secondly as the Variance of the the Sample Mean is mu/(n-1) this leads us to believe that the Variance will fall as we increase the sample size. Variance is a measure of the dispersion of values collected from the centre of the data. Where the centre of the data is a fixed value equal to the median. Put Bias and Variance together and you get the Mean Squared Error which is the error associated with using an estimator of the Population Mean. The formula for Mean Squared Error = Bias^2 + Variance With our estimator we can see that as the Bias = zero, it has no relevance to the error and as the variance falls as the sample size increases then we can conclude that the error associated with using the sample mean will fall as the sample size increases. Conclusions: The Random Sample of public opinon will on average lead to a true representation of the Population Mean and therefore the random samle you have will represnt the public opinion to a fairly high degree of accuracy. Finally, this degree of accuracy will rise incredibly quickly as the sample size rises thus leading to a very accurate representation (on average)
What is sample taken without bias?
When a p-n junction is taken without a bias, it forms a PHOTO VOLTAIC CELL.
