The primary objective of point estimation is to use sample data to obtain an estimate of the unknown population parameter, rather than finding its exact value or testing hypotheses about it.

The sample mean is a point estimator for the population mean. It is calculated by summing the values in a sample and dividing by the sample size.

The sampling distribution of a point estimator describes the distribution of the estimator in repeated samples of the same size. It provides information about the estimator's variability and its accuracy.

Unbiasedness is the property of a point estimator that ensures that its expected value is equal to the population parameter. This means that, on average, the estimator will produce estimates that are close to the true value of the parameter.

A confidence interval provides a range of plausible values for the population parameter based on sample data. It is constructed using the point estimator and the sampling distribution of the estimator.

The width of a confidence interval is affected by the sample size, the level of confidence, and the variability of the population. A larger sample size, a higher level of confidence, and a more variable population will all lead to a wider confidence interval.

In general, point estimation is a necessary step before hypothesis testing. This is because the point estimate provides an initial estimate of the population parameter, which is then used to formulate and test the hypothesis.

All of the given estimators (sample mean, sample median, and sample proportion) are consistent estimators. This means that as the sample size increases, the estimates obtained from these estimators will converge to the true value of the population parameter.

The efficiency of an estimator is measured by the ratio of the variance of the estimator to the variance of the population parameter. A more efficient estimator will have a smaller variance and will produce estimates that are closer to the true value of the parameter.

A sufficient statistic is a statistic that contains all the information about the population parameter that is contained in the entire sample. This means that, given a sufficient statistic, no additional information can be obtained from the sample regarding the population parameter.

The sample mean is a sufficient statistic for the mean of a normal distribution. This means that, given the sample mean, no additional information can be obtained from the sample regarding the mean of the population.

The Neyman-Pearson lemma provides a method for constructing uniformly most powerful tests of hypotheses. It is a fundamental result in statistical inference and is used to derive optimal tests for various statistical problems.

The sample proportion is an example of a maximum likelihood estimator. It is the value of the population proportion that maximizes the likelihood function of the sample data.

The method of moments is a method for constructing unbiased estimators. It involves equating the sample moments to the corresponding population moments and solving for the unknown population parameters.

The posterior mean is an example of a Bayesian estimator. It is the expected value of the population parameter given the observed sample data and prior information.