STATS

• Sample: the selected part of population
• Sample Size: the number of people in the selected sample
• Sampling Frame: the list of individual or people included in the sample
• Sampling: the process of selecting a part of the population
• Sampling Technique: the technique or procedure used to select the members of the sample. There are various types of sampling techniques
• Sampling means the process of selecting a part of the population
• A population is a group people that is studied in a research. These are the members of a town, a city, or a country.
• It is difficult for a researcher to study the whole population due to limited resources
• Random Sampling- -a selection of 𝑛 elements derived from a population 𝑁, which is the subject of investigation where each sample point has equal chance of being selected
• • Lottery Sampling (Simple Random)is a method of probability sampling in which every unit or member of the population has an equal non zero chance of being selected for the sample.
• • . Systematic Sampling technique in which members of the population are listed and samples are selected in intervals called sample intervals. In this technique, every 𝒏𝒕𝒉element from the list is selected from a randomly selected starting point
• • Stratified Random Sampling procedure wherein the members of the population are grouped based on their homogeneity. This technique is used when there are a number of distinct subgroups in the population, within each of which is required that there is full representation.
• • Cluster Sampling (Area Sampling)It is applied on a geographical basis. It is generally done by first sampling at the higher levels before going down to lower levels
• • . Multi-stage Sampling Done using a combination of different sampling techniques.
• • Sampling Distribution The probability distribution that describes the probability for each mean of all samples with the same sample size “n” .
• Sampling with Replacement:

  In this method, after you select an item, you put it back into the population before drawing the next one. This means each item has an equal chance of being picked on every draw, regardless

  of whether it was chosen before.
• Sampling Without Replacement: Here, once you pick an item, you don’t put it back into the population. This affects the probability as the remaining items have a higher chance of being chosen in subsequent draws.
• • CENTRAL LIMIT THEOREM If samples of size n, where n is sufficiently large, are drawn from any population which a mean u, and a standard deviation o, then the sampling distribution of sample means approximates a normal distribution
• • The sampling distribution of the sample means taken with replacement from a population N with population mean u, and variance o, will approach a normal distribution according to central limit theorem