**method used to cull a smaller sample size from a larger population and use it to research and make generalizations about the larger group**. … The advantages of a simple random sample include its ease of use and its accurate representation of the larger population.

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**switch case in java example programs with user input**.

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The simplest random sample **allows all the units in the population to have an equal chance of being selected**. … Perhaps the most important benefit to selecting random samples is that it enables the researcher to rely upon assumptions of statistical theory to draw conclusions from what is observed (Moore & McCabe, 2003).

Simple random sampling is most appropriate **when the entire population from which the sample is taken is homogeneous**.

A simple random sample **takes a small, random portion of the entire population to represent the entire data set**, where each member has an equal probability of being chosen. Researchers can create a simple random sample using methods like lotteries or random draws.

A simple random sample is one of the methods researchers use to choose a sample from a larger population. Major advantages include **its simplicity and lack of bias**.

- Step 1: Define the population. Start by deciding on the population that you want to study. …
- Step 2: Decide on the sample size. Next, you need to decide how large your sample size will be. …
- Step 3: Randomly select your sample. …
- Step 4: Collect data from your sample.

Random Sampling | |

Advantages Free from researcher bias Prevents from choosing people who may support their hypothesis | Disadvantages Time consuming May end up with an unrepresentative sample Some may refuse to take part |

Evaluation Most fair way of sampling, however it may be unrepresentative of the population |

There are several different sampling techniques available, and they can be subdivided into two groups: **probability sampling and non-probability sampling**.

- Simple Random Sampling. Simple random sampling requires using randomly generated numbers to choose a sample. …
- Stratified Random Sampling. …
- Cluster Random Sampling. …
- Systematic Random Sampling.

Non-random sampling is a **sampling technique where the sample selection is based on factors other than just random chance**. In other words, non-random sampling is biased in nature. Here, the sample will be selected based on the convenience, experience or judgment of the researcher.

- Define the population. …
- Choose your sample size. …
- List the population. …
- Assign numbers to the units. …
- Find random numbers. …
- Select your sample.

Choose your sample from all the households. Avoid choosing samples which might result in biased estimates. To avoid bias you should use **probability sampling** to select your sample of respondents. Bias depends on the selection procedure, not on sample size.

In probability sampling, the sampler chooses the representative to be part of the sample randomly, whereas, in non-probability sampling, **the subject is chosen arbitrarily, to belong to the sample by the researcher**. The chances of selection in probability sampling, are fixed and known.

One of the most common examples of convenience sampling within developmental science is the use of student volunteers as study participants. The key advantages of convenience sampling are that **it is cheap, efficient, and simple to implement.**

Advantages of non-probability sampling Getting responses using non-probability sampling is **faster and more cost-effective than probability sampling** because the sample is known to the researcher. The respondents respond quickly as compared to people randomly selected as they have a high motivation level to participate.

Each selection has an equal likelihood of being chosen as a part of the sample. If simple random sampling occurs “without replacement,” as it often does, that means **the chosen participant cannot be returned to the population and drawn again**.