The most important criteria for probability sampling is that everyone in your population has an equal and known chance of getting chosen. For example, in a population of 100 persons, each person has a one-in-100 chance of being chosen. This means that there are 100 different ways that one person can be selected out of the population.

Because probability sampling requires selecting individuals from the population, it is not possible to obtain a complete list of all members of the population. Thus, it is impossible to do probability sampling when the goal is to obtain a representative sample of **the whole population**.

However, probability sampling is very useful when we want to study a subgroup of the population - such as children attending **a particular school** or patients seen at a particular clinic. In this case, we cannot simply choose names out of a hat; but rather we use some method to identify these groups of people and then select participants with replacement, so they will never appear again (this is important for statistical reliability).

For example, if we wanted to estimate how many students attend **Roosevelt High School**, we would first decide what proportion of the population was attending school. Then we would calculate the percentage of students at Roosevelt High School by dividing the number of students attending Roosevelt High School by **the total number** of students in the population.

Probability sampling provides the best opportunity to produce a sample that is really representative of the population. As a general guideline, your sample size should be bigger than 30. If your sample size is tiny, you may need to use one of the non-probability sampling approaches instead.

In addition to being representative, another advantage of probability sampling is its ability to provide precise estimates of statistics such as mean and variance. Since each member of the sample has an equal chance of being selected, statistical inference about the population can be done directly from the sample. There are many other advantages as well, such as allowing for control over bias and precision. Probability sampling is widely used in scientific studies because it provides reliable results while minimizing cost and time consumption.

There are two types of probability samples: simple and complex. In **simple random samples**, each unit (or respondent) has an equal chance of being selected. This is not always the case in **complex samples**, but we will discuss these later. Simple random samples are easy to implement and inexpensive to conduct. They also yield reliable results when applied properly. However, like any other sampling method, they can be problematic if not implemented correctly; for example, if respondents feel obliged to answer questions about their household or if there are significant differences between respondents and non-respondents. For **these reasons** and more, simple random sampling is recommended only when sufficient information is available on which to base a choice of sampling method.

What criteria must be satisfied before probability sampling may take place? The whole population must be identified. There must be a sample of the population. The sample should be selected by random procedure. The sample frame must be complete.

These questions relate to what is known as theoretical validity. Theory refers to our understanding of how things work out in reality. In other words, theory is based on evidence from past experiences and tries to make predictions about what will happen in new situations. Statistical theories are descriptions of how data are collected and analyzed that allow us to make meaningful statements about populations from which samples are taken. In practice, there is no way to prove whether or not a statistical method will produce reliable results until we try it out. Therefore, any statistical technique may be used provided that it is properly applied.

Sampling is a useful tool in statistics because it allows us to make generalizations about a population when only a small part of **this population** can be observed. For example, if we wanted to estimate the number of birds in **a large forest**, we could choose **some random trees** and count the birds that were living in them.

A probability sample exists when each element in a population has a chance of being in a sample and the researcher can estimate what that chance is. A significant disadvantage of utilizing simple random sampling is that it is frequently impracticable and costly with a big sample frame. For example, if there are thousands or millions of people in a country, it would be impractical to randomly select one person from **the entire population**. Instead, statistical methods must be used to derive a representative sample.

There are two main disadvantages of using probability sampling for a quizlet: accuracy and reliability. Accuracy refers to the degree to which a measurement reflects the actual state of affairs. In statistics, accuracy is measured by how closely an estimated value matches the actual state of affairs. Statistics cannot measure exact values because they are estimates based on samples of data. However, statistics can provide very accurate estimates for large groups of people if those groups are selected through a process called randomization. Randomization means selecting individuals or items without any consideration of specific characteristics such as age, gender, or income level. This ensures that observed differences between groups are actually due to these factors and not due to other unmeasured variables such as race or income class.

When estimating the size of a population, statisticians usually use **both qualitative and quantitative methods**. Qualitative methods include simply asking people about **their experiences** (i.e., a survey).

Sampling Based on Probability The term "probability sampling" refers to the fact that every member in a population has a chance of being chosen. Because probability sampling is based on random selection, any subset of the population has an equal chance of being represented in the sample. This means that probability samples can be used to represent groups of people as well as individual respondents.

In survey research, probability sampling is often done through random selection from a list or directory of names. For example, a researcher might contact each household on a mailing list and ask if they would like to take part in **an interview study**. If the respondent agrees, then they will be included in the sample. There are many ways to select participants for a study. For example, a researcher could choose individuals by drawing names out of a hat, but this would not be considered probability sampling because there is no way to ensure that the sample was representative of the entire population.

In experimental studies, probability sampling is usually done through **random assignment** to conditions or treatments. For example, researchers might assign students in a class to read one of two different textbooks. Those students who were assigned to read the textbook have greater chances of being selected for the sample than those who were not given a choice. In **this case**, the sample is representative of the entire class.