Sample in statistics means a small part of the targeted population. A sample must be representative of the population. Sampling methods must be used to achieve a representative sampling. Otherwise, the sampling does not represent the targeted population and the results yield biased or inaccurate information.

Error in population specification occurs when the researcher determines an inappropriate population from which to collect data.

Error in measurement can be described as any discrepancy between the actual result obtained and the correct result that would be provided by an ideal procedure. From a statistical point of view any observation is composed of the true value plus some random error value. However, all error is not random. The error component of any observation can be divided into two subcomponents, random error and systematic error.
Random errors are caused by unknown and unpredictable factors that randomly affect measurement of the variable across the sample. For example, a school teacher conducted a particular survey on the students to measure their performances. Some students may be feeling in a good mood and others may be depressed. This may artificially deflate performance scores of the depressed students. Random error does not have any consistent effects across the entire sample. Instead, it affects observed scores up or down randomly. Random error adds variability to the data and it is sometimes called noise.
Systematic errors are reproducible inaccuracies that shift measurements from their true value by the same amount and consistently in the same direction. This type error arises from problematic, poor calibrated or incorrectly used equipment. For example, an industrial scale showed heavier weights than it should be for a particular product because it was not calibrated properly and thereby provided incorrect measurements.