**7. Sampling errors**

Sampling errors are statistical errors, they arise because only a fraction of population has been observed. Different samples will give different results. Sampling errors become less important as the sample size increases [26].

$$\text{Sampling Error} = Z \propto \left(\sigma/\sqrt{n}\right) \tag{2}$$

(sampling Error is calculated by dividing the standard deviation of the population by the square root of the size of sample and then multiplying the resultant with the Z score value which is based on confidence interval).

Step by Step Calculation of Sampling Error:

Collected all the data set called the population. Calculate population mean and population standard deviation.

Determine the confidence level and, as a result, the value of the Z score can be calculated from its table.

Now multiply the Z score by the population standard deviation and divide the same by the square root of the sample size in order to arrive at an error margin or an error in the sample size.

#### **8. Non-sampling errors**

They arise if the sampling procedure is not representative of the total population. Such errors do not necessarily decrease as sample size increases. Examples of this type of error are the failure to include people with no permanent home because their existence is not recorded, or the refusal of some individuals to participate in the study. These errors constitute *bias* [27]*.*
