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Population mean is the average of all elements in a particular population, whereas a sample mean is the average of elements in a representative sample drawn from the population. The population mean is denoted by the symbol μ (mu), while the sample mean is denoted by the symbol x̄ (x-bar).

In statistical analysis, we generally base our conclusions about a population based on a sample drawn from the population as it is often impractical or impossible to obtain data from the entire population. The larger the sample size, the more accurate our estimation of the population mean would be when carrying out statistical analysis. If we have a truly random and highly representative sample, the sample mean should be close to the population mean, and as the sample size approaches the population size, the sample mean gets closer to the population mean.

Slim's statement that "the population mean is just the mean of a suitably large sample" is partially accurate. While it is true that the sample mean approaches the population mean as the sample size increases and becomes more representative of the population, it is not guaranteed that the sample mean will equal the population mean. In practice, the sample mean is often used as an approximation of the population mean, but one should always keep in mind that there can still be a degree of uncertainty or potential for error. In conclusion, Slim's statement is not entirely correct, as the population mean is not just the mean of a suitably large sample. The sample mean is an estimation of the population mean, and while it can get closer as the sample size increases, there is no guarantee that the sample mean will equal the population mean.