Resampling
Resampling refers to the act of repeatedly drawing samples from the original observed data sample for the statistical inference of population parameters. The following are the two commonly used methods of resampling: Bootstrap and jackknife. Bootstrap In the bootstrap resampling…
Considerations and Biases in Sampling
Sampling considerations refer to the desirable characteristics that should always be taken into account when selecting a sample, which in turn increase the chances of accurately estimating the population parameters. In general, larger samples are preferred to smaller ones. This…
Confidence Intervals
Confidence interval (CI) refers to a range of values within which statisticians believe the actual value of a certain population parameter lies. It is different from from a point estimate which is a single, specific numerical value.
Point Estimate and Confidence Interval Estimate
Point Estimate A point estimate gives statisticians a single value as the estimate of a given population parameter. For example, the sample mean X̄ is the point estimate of the population mean μ. Similarly, the sample proportion p is a…
Properties of an Estimator
A point estimator (PE) is a sample statistic used to estimate an unknown population parameter. It is a random variable and therefore varies from sample to sample. A good example of an estimator is the sample mean, \(x\), which helps…
Standard Error of the Sample Mean
The standard error (SE) of the sample mean refers to the standard deviation of the distribution of the sample means. It gives analysts an estimate of the variability they would expect if they were to draw multiple samples from the…
Central Limit Theorem
The central limit theorem asserts that when we have simple random samples, each of size n from a population with mean μ and variance σ2, the sample mean X approximately has a normal distribution with mean μ and variance σ2/n…
Comparing Probability and Non-Probability Sampling Techniques
You will recall that simple random sampling, stratified random sampling, and cluster sampling are types of probability sampling techniques. On the other hand, convenience sampling and judgemental sampling are types of non-probability sampling techniques. Probability Sampling Methods Simple Random Sampling…
Sampling Error Explained
Sampling error is the statistical error that occurs when an analyst selects a sample that is not representative of the population as a whole. In other words, it is the difference between the observed value of a sample statistic (mean,…
Probability and Non-Probability Sampling
A population is the total number of elements in a group while a sample is a portion of the population. Sample statistics—quantities such as sample mean that describe sample data—generalize the information about the population parameter. As such, we draw…
