That's because the center of the sampling distribution represents the best estimate of the population average, and the population is what you want to make inferences to. You can use some of the information you've collected thus far to calculate the sampling distribution, or more accurately, the sampling error . In sampling, the standard error is referred to as sampling error . You never actually see the sampling distribution. All you have to work with is the standard deviation of your sample.
Standard error is also related to sample size.
The larger your sample, the smaller the standard error. You're not reducing bias or anything by increasing sample size, only coming closer to the total number in the population. Validity and sampling error are somewhat similar. However, you can estimate population parameters from even small samples. The best way to estimate population parameters is to use a confidence interval approach.