Confidence interval
Significance levels tell the researcher how likely a finding is the result of chance. Generally, researchers use the 0.95 (or 95%) confidence level to denote that a result is reliable. This means, in order to use a sample, as opposed to a census, we accept the risk of reaching wrong conclusions 5 times out of 100.
Error level
When interpreting the results of a survey, the researcher has a large number of tables of frequencies and percentages to examine. These results, being based on a sample, will be subject to sampling errors. The error levels LogRatio computes measure exactly these errors for a whole table as well as for the single columns and rows.
Random recruitment
LogRatio assumes the recruitment of respondents to a survey was conducted in a random manner. That is, every component of the population from which the sample is extracted has the same probability of being chosen.
Sample size
Is the sample size big enough? Does it provide results of sufficient statistical reliability to detect differences in the data which are not simply the result of casual variation?
Market researchers are well aware that it is not size that makes a sample representative of the population it comes from.
What really matters is to avoid gathering biased samples. Most often bias occurs when the respondent selection is, in some way, influenced by distorting factors, like human pre-conceptions and inability to screen sample components, e.g. of online surveys.
Sampling
Sampling is the act of selecting a given number of items, or persons, from a certain population. There are different ways of extracting samples: Random sampling, Systematic, Stratified, Quota, and others.
LogRatio assumes the survey sample it analyzes is a random one.