Match the vocabulary word to its correct definition. 1. mean
The mean is the average value of the data you collect. In statistics, you find the mean of your sample. Then you use that mean to approximate the mean of the entire population. If you want the mean of the data to be a good approximation of the mean of the entire population, your need to make sure that you collect a random sample. To find the mean, you find the sum of the data points you collected and divide it by the number of data points in the set. For example, if the data you collected was {3, 3, 6}. Then the mean would be (3 + 3 + 6) ÷ 3 = 12 ÷ 3 = 4. It's important to note that the mean does not have to be a data point in your set. You can generalize the formula as well. If you collect n data points {d1, d2, d3, … dn}, then the mean of your data set is . The Greek letter μ—pronounced "mu"—is used because the word "mean" begins with the letter "m." It is important that you divide by the number of data points in your set, n.
2. population
An experiment is a well-defined procedure under controlled conditions designed to gather data that tests a hypothesis. For example, you want to test the hypothesis that flowers grow faster when they are watered every other day as opposed to every day. Then you could water one set of plants every day and another set of plants every other day. What data would you collect? The height of each plant once a day. What conditions would you need to control? You would need to make sure that the groups of plants are treated the same in every other way by getting the same amount of sun and being maintained at the same temperature, etc.
3. experiment
Bias is the preference for a particular group or subset. In the example above, the bias was toward viewers who like sports. When your sample is not chosen at random, you introduce bias into your study. So, in a certain sense, a biased sample is the opposite of a random sample. Why? In a biased sample, some members of the population are more likely to be chosen than others. In a random sample, every member of the population is equally likely to be chosen. Bias may also occur in surveys. See "survey" below for more information on this type of bias.
4. sample
A population is the entire group you want to understand. So, in a certain sense, you can think of the population as the "opposite" of the sample because a sample is not the entire population. A sample is chosen subset upon that you use to draw conclusions about the entire population.
5. bias
Random means free from bias. So, if you choose a random sample from a population, every member of the population has an equal chance of being chosen. It should be pointed out that this "true randomness" is an ideal which is difficult to achieve in real world experiments conducted on real world populations. That is why statisticians make every effort to ensure their samples chosen as free from bias as is possible, that is, as randomly as possible under real world conditions.
6. survey
A sample is a subset chosen from a population. In most cases, you want a random sample. Why? A random sample has the best chance of accurately representing the entire population.
7. random
A survey is the means of collecting a sample. In statistics, a survey is often a question or set of questions. It is critical that the questions, like the sample itself, be free from bias. Bias in a question tends to favor one response. As an extreme example of a biased question about colors might be, "Don't you think blue is beautiful and yellow is yucky?" An unbiased form of the question might be, "Which color do you prefer, blue or yellow?" Though careful, statisticians might be concerned about the order in which the colors are offered, so that might further simplify the question to, "What is your favorite color?" This type of question is most free from bias because it does not suggest a response and therefore does not favor a response.

1. mean-The mean is the average value of the data you collect. In statistics, you find the mean of your sample. Then you use that mean to approximate the mean of the entire population. If you want the mean of the data to be a good approximation of the mean of the entire population, your need to make sure that you collect a random sample. To find the mean, you find the sum of the data points you collected and divide it by the number of data points in the set. For example, if the data you collected was {3, 3, 6}. Then the mean would be (3 + 3 + 6) ÷ 3 = 12 ÷ 3 = 4. It's important to note that the mean does not have to be a data point in your set. You can generalize the formula as well. If you collect n data points {d1, d2, d3, … dn}, then the mean of your data set is . The Greek letter μ—pronounced "mu"—is used because the word "mean" begins with the letter "m." It is important that you divide by the number of data points in your set, n.

2. Population-A population is the entire group you want to understand. So, in a certain sense, you can think of the population as the "opposite" of the sample because a sample is not the entire population. A sample is chosen subset upon that you use to draw conclusions about the entire population.

3. Experiment-An experiment is a well-defined procedure under controlled conditions designed to gather data that tests a hypothesis. For example, you want to test the hypothesis that flowers grow faster when they are watered every other day as opposed to every day. Then you could water one set of plants every day and another set of plants every other day. What data would you collect? The height of each plant once a day. What conditions would you need to control? You would need to make sure that the groups of plants are treated the same in every other way by getting the same amount of sun and being maintained at the same temperature, etc.

4. Sample-A sample is a subset chosen from a population. In most cases, you want a random sample. Why? A random sample has the best chance of accurately representing the entire population.

5. Bais-Bias is the preference for a particular group or subset. In the example above, the bias was toward viewers who like sports. When your sample is not chosen at random, you introduce bias into your study. So, in a certain sense, a biased sample is the opposite of a random sample. Why? In a biased sample, some members of the population are more likely to be chosen than others. In a random sample, every member of the population is equally likely to be chosen. Bias may also occur in surveys. See "survey" below for more information on this type of bias.

6. Survey-A survey is the means of collecting a sample. In statistics, a survey is often a question or set of questions. It is critical that the questions, like the sample itself, be free from bias. Bias in a question tends to favor one response. As an extreme example of a biased question about colors might be, "Don't you think blue is beautiful and yellow is yucky?" An unbiased form of the question might be, "Which color do you prefer, blue or yellow?" Though careful, statisticians might be concerned about the order in which the colors are offered, so that might further simplify the question to, "What is your favorite color?" This type of question is most free from bias because it does not suggest a response and therefore does not favor a response.

7. Random-Random means free from bias. So, if you choose a random sample from a population, every member of the population has an equal chance of being chosen. It should be pointed out that this "true randomness" is an ideal which is difficult to achieve in real world experiments conducted on real world populations. That is why statisticians make every effort to ensure their samples chosen as free from bias as is possible, that is, as randomly as possible under real world conditions.

Step-by-step explanation:

It says what each of them are in each paragraph, you just need to match them up.

Hope this helps :)

i need points

step-by-step explanation:

sorry bubs

1/2(2*4)=6 $$- agahajakanajak