What are the factors that affect the average error of the sampling

Definition of Sampling Mean Error:

The sampling mean error is the sampling mean.

or standard deviation of the sampling percentage.

It reflects the average degree of error between the sampling index and the population index.

Most of the sample indicators and the overall indicators have errors, the errors are large, small, positive, negative, sampling average error is to synthesize all the errors, and then find the average, so the sampling average error is to reflect the sampling error.

Indicators of the general level.

Factors influencing the average error of sampling:

1. The degree of change of the whole and overall signs.

The greater the variation of the whole and overall signs, the greater the sampling mean error. Conversely, the smaller the all-and-total flags, the smaller the eastern, the smaller the sampling error. The change in the relationship between the two is directly proportional.

2. The number of sampling units.

In the case of other conditions, the more units are extracted, the smaller the sampling average error. The smaller the number of sample units, the greater the sampling average error. The change in the size of the average error of the sample and the number of sample units is inverse, because the more the number of sampling units, the higher the proportion of the number of sample units in the whole and the population, the closer the sampled population will be to the basic characteristics of the whole population, and the more the characteristics of the population can be truly reflected in the sampled population.

3. The way of sampling organization.

In addition to the above two factors, the average error of sampling is also affected by different sampling methods.

4. The organizational form of the sampling survey.

The sampling error of sampling surveys is different depending on the organizational form, and the degree of reasonableness of the same organizational form will also affect the sampling error.

1. The number of sampling units. Other things being equal, the greater the number of sampling units, the smaller the sampling error. The smaller the number of sampling units, the greater the sampling error. This is because as the sample size increases, the closer the sample structure is to the population.

The sample survey will also be closer to a full survey. When the sample is expanded to the population, it is a comprehensive survey and there is no sampling error.

2. The degree of variation in the overall marker being studied。Other things being equal, the smaller the degree of variation in the overall marker, the smaller the sampling error. The greater the degree of variation in the overall marker, the greater the sampling error.

Sampling error varies directly proportional to the degree of variation in the population marker. This is because the population has a small degree of variation, which means that there is little variation between the flag values of each unit in the population. The difference between the sample index and the population index may also be small; If the flag values of each unit in the population are equal, the flag variation is zero, and the sample index is equal to the population index, and there is no sampling error.

3. Selection of sampling methods. The magnitude of the sampling error is different for repeated and non-repeated sampling. The sampling error of non-duplicate sampling is smaller than that of repeated sampling.

4. The sampling organization is different. Different organizations will have different sampling errors, because the samples sampled by different sampling organizations are also different for the population. In general, we do not often use different sampling errors to make a comparison standard for judging the various sampling organization methods.

Sampling error: refers to the inevitable error caused by the use of sample indicators to represent the population indicators under the condition that the principle of randomness is followed, excluding registration errors and systematic errors. Since the population mean and population percentage are determined, while the sample mean and sample percentage are random variables, the sampling error is also a random variable.

The smaller the sampling error, the higher the representativeness of the sample. Conversely, the less representative the sample is. At the same time, the sampling error also indicates the range of differences between the sample index and the population index, so it is the basis for extrapolating the population index.

Sampling error is inherent in statistical inference and, although unavoidable, can be calculated using mathematical formulas. The specific quantitative limits are determined and controlled by the sampling design procedure, so the sampling error can also be called a controllable error.

The sampling average error is an index that reflects the general level of sampling error, and its essence refers to the standard deviation of the sample average tolerance number (or component). That is, it reflects the average degree of deviation between the sampled indicators and the population indicators.

Sampling inference is based on the principle of randomness to extract part of the actual data from the population, and use mathematical statistical methods to estimate the quantity of a certain phenomenon in the population with a certain degree of reliability.

Sampling inference has these characteristics: it is an epistemic method of extrapolating the whole from parts, and it is based on random sampling. It is a method that uses probability estimation, and the error of sampling inference can be calculated and controlled in advance.

The factors that affect sampling error are:

the degree of variance in the overall unit marker values; the number of units of the sample; methods of sampling; The form of organization of the sample survey.

1. Sampling average error. The effect of the sampling average error delay is first manifested in the fact that it can explain the representativeness of the sample index. The average error is large, indicating that the sample index is low in representativeness to the overall index, and vice versa, it is high.

2. Sampling limit error. The sampling limit indicates that the sample index is highly representative of the overall index. Second, the average error also illustrates the general range of differences between the sample and the population indicators. This range is actually the sampling limit error.

The main factors affecting the sampling average error are:

1. The degree of difference in the marker value of the overall unit: the greater the degree of difference, the greater the sampling error, and the smaller the source of the mask;

2. The number of sample units: other things being equal, the larger the number of sample units, the smaller the sampling error;

3 Sampling method: The sampling error is different depending on the sampling method. In general, repeated sampling is more inaccurate than non-repeated sampling;

4. Organizational form of sampling survey: The organizational form of sampling survey is different, and the sampling error of object guessing state is also different, and the reasonableness of the same organizational form will also affect the sampling error.

The main factors affecting the sampling average error are:

1. The degree of difference in the marker values of the population unit: the greater the difference, the greater the sampling error, and vice versa;

2 The number of sample units: the larger the number of sample units, the smaller the sampling error, when other conditions are similar;

3. Sampling method: The sampling error is different depending on the hail sampling method. In general, repeated sampling is more inaccurate than non-repeated sampling;

4 Organizational form of sampling survey: The sampling error of sampling surveys is different for different organizational forms, and the degree of reasonableness of the same organizational form will also affect the sampling error.

The factors influencing the average error of the sample are briefly described.

A:First, the degree of variation in the overall unit mark. The greater the degree of overall marker variation, the greater the sampling error.

Conversely, the smaller the degree of variation in the overall marker, the smaller the sampling error. second, the size of the surplus capacity of the sample; third, the impact of different sampling methods; Fourth, the influence of different bucket roll organization methods.

a.Mean of the population variables.

b.The average of the sample variables is called.

c.Standard deviation of the overall indicator is guessed.

d.Standard deviation of the mean of all possible samples.

Correct Answer: Negative Targeting Type C

The influencing factors of sampling error are as follows:

Sampling error and estimation error are two important concepts in statistics, and there is a certain relationship between them.

Sampling error refers to the error that occurs when the sample data may not fully reflect the true situation of the population due to the random nature of the sample. In other words, sampling error is the error caused by the use of the sampling roll answer method. The magnitude of the sampling error is affected by many factors, including population size, sample size, sampling method, etc.

Estimation error refers to the error that still exists due to the limitations of the sample data when estimating the population using the sample number scatter data. When using sample data to estimate population data, the resulting estimates often have some error due to sample size limitations or due to the incompleteness of the sample data.

The relationship between the two is that estimation error is a manifestation of sampling error, because the size of the estimation error is affected by factors such as sample size and the choice of sampling method, which are one of the factors that lead to sampling error. Therefore, when making estimates, we need to evaluate the reliability of the estimation results by considering the sampling error and estimation error, and adopt appropriate sampling methods and sample sizes to reduce the impact of the error and improve the accuracy and reliability of the estimation results.