random sample

This is called a random sample.

In the context of a sample of size n out of a population of N, any sample of size n has the same probability of being selected. This is equivalent to the statement that any member of the population has the same probability of being included in the sample.

When each member of the population has the same probability of being selected as a member of the sample.

Each member of the population must have the same probability of being included in the sample. Equivalently, each set of elements comprising a sample must have the same probability of being selected.

Random sampling is a method of selecting a sample where each member of the population has the same probability of being included in the sample. An equivalent statement is that each subset of the population, of the given size, has the same probability of being selected as any other subset of that size.

Simple random sampling.

Systematic sampling

There are two equivalent ways of defining a simple random sample from a larger population. One definition is that every member of the population has the same probability of being included in the sample. The second is that, if you generate all possible samples of the given size from the population, then each such sample has the same probability of being selected for use.

A probability sample is one in which each member of the population has the same probability of being included. An alternative and equivalent definition is that it is a sample such that the probability of selecting that particular sample is the same for all samples of that size which could be drawn from the population.

Every member in the population has the same probability of being in the sample.Or, equivalently, every set of the given sample size has the same probability of being selected.

It means that every member of the population has the same probability of being included in the sample.

Each member of the population has the same probability of being in the sample as any other. Equivalently, any set of members of the given sample size has the same probability of being selected as any other set.

With a probabilistic method, each member of the population has the same probability of being selected for the sample. Equivalently, given a sample size, every sample of that size has the same probability of being the sample which is selected. With such a sample it is easier to find an unbiased estimate of common statistical measures. None of this is true for non-probabilistic sampling.

Probability sampling, according to which, a member of the population has the same probability of being included in the sample as any other member. Equivalently, each sample of a given size has the same probability of being chosen.Stratified and cluster sampling are variations on this idea. In stratified sampling, the population can be divided up into strata such that members within each stratum are more like each other than across strata. One example may be school pupils in different year groups. A sampling scheme could assign a number to be sampled from each stratum (perhaps according to how large that group is) and then, within that stratum, to use simple probability sampling.Cluster sampling is used when the entire population can be split up into clusters. Clusters are selected using probability sampling. Then a census is used within each cluster. For example, if you wanted to sample schools across the country, a simple probability sample would result in schools all over the country and the travelling costs (and time) would be prohibitive. Instead, you divide the country up into regions and take a probability sample of these regions. You end up visiting every school within the few selected regions.

It is usually impossible to work out exactly how large population will behave in any given circumstance. However, it may be possible to assign probabilities of different responses to each member of the population and then, using the rules of probability, calculate how the population - as a whole, or on average - is likely to behave.

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A simple random sample is a method of selecting a sample where the probability of any particular member of the population being part of the sample is the same for all members of the population.

Altruism is basically when a member sacrifices itself for the well being of the population. If the population that was protected has favorable traits, they will be naturally selected for and over generations become dominant in the population. If they weren't protected, the population could die out.

Simple random sampling = A process of selecting subjects in such a way that each member of the population has an equal likelihood of being selected; you can throw all your subjects into a hat and draw them out one by one, or assign each member a number and choose every fifth number to be a participant.Probability sampling=A sampling procedure in which the probability that each element of the population will be included in the sample can be specified; you have a specific number of subjects and you know that they have a 50/50 chance of being chosen, or because of an anomaly, they may only have a 20/100 chance of being chosen for the experiment.*Your teacher is being tricky however, because there are 4 basic types of Probability sampling and simple random sampling is one of them. Also are stratified, systematic and cluster sampling. All four fall under the general title of Probability Sampling (P.S.)!! P.S. is kinda like the category and the 4 types are just different ways to do the sample, each has their own "little differences" in how the data is collected and assigned.

systematic- a member of the population is selected at random convenience- the most-available members of the population are chosen self-selected- members of the population volunteer to respond to a survey. Note: Biased questions- Example what about a new subway 85%yes,15%no. This question is biased because only people who ride the subway would say yes. -information provided by HOLT

single-member districts

single-member districts

g.k. gokhle

Nachos