In statistic, point estimation comprises of the use of sample data to estimate a distinct data value (known as a point estimate) which is to function as a "best guess" or "best estimate" of an unidentified population parameter.

The point estimate of the population mean (µ) is the sample mean ( $\bar x$ ).

Now according to the law of large numbers, in probability concept, states that as we increase the sample size, the mean of the sample ( $\bar x$ ) approaches the whole population mean (µ).

That is, $\bar x$ → µ as n → ∞.

The scientist uses the weights of 25 fish from the pond to find the average weight.

The sample is large, but not large enough.

So, use interval estimation to compute the average weight of fish living in a large pond.

The interval estimation is the best way to estimate the true value of a parameter. This is because the interval of values computed for the parameter has certain probability of containing the true parameter value.

The (1 - α)% confidence interval for population mean is an interval estimate.

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3 years ago