Question

Identify each of the following as a source of bias, sampling error or neither

1. using whatever individuals happen to be available as your sample

2. using a very small value for \alpha for a hypothesis test

3. removing the largest and smallest data from your sample

Answer 1

sampling error is error caused by taking sample from population to estimate population statistics easily . w

we take sample instead of using whole sample for survey ,then there exists always a difference between actual population and sample .this difference is known as biasedness .

due to sampling error result of census are different than samples.because it is not possible that samples selected has equal characteristics to population . sample describes almost all properties of population but not all.

now , according to question :-

(1)- if we use sampling in such a way that we select sample which is easily available then this can lead to sampling error in data collected

(2)- if alpha= 5% then this means that on

Assuming that the null hypothesis is true, we may reject the null hypothesis if the observed
data are so unusual that they would have occurred by chance at most 5 % of the time. T

smaller the alpha, the more stringent the test (the more unlikely it is to find a statistically
significant result).

hence it is neither source of biasedness nor source of sampling error

(3)- extreme value are generally source of biasedness in sample survey . then we generally remove these extreme values from data ,because these values may lead to skewness in data .

hence it is source of bias.

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