What are the factors that cause the error and what are the factors that affect the standard error

The tools are not precise enough, and everyone's loneliness value is different.

The factors that affect the standard error are: the method of sampling, the method of sampling, the size of the sample size, and the degree of difference between individuals in the population.

In the finite number of measurements, the root mean square error is often expressed by the following formula: [di 2 n]=re, where n is the number of measurements; di is the deviation of a measured value from the true value.

If the statistical distribution of errors is normally distributed, then the probability that the random error falls within is 68%.

Examples of standard errors:

For example, two sets of samples:

The first group had the following three samples: 3, 4, 5

The second group had the following three samples: 2, 4, 6

The mean of both groups is 4, but the three values of the first group are relatively closer to the mean, that is, the degree of dispersion is small, which is represented by the mean square deviation.

Similarly, variance, standard deviation (the root of variance is rooted, because the units are not uniform) are all representations of the degree of dispersion of the data.

Standard error (English: standard error) is a measure of the size of the sampling error of the corresponding sample statistic.

Standard error is used to measure sampling error. The smaller the standard error, the closer the sample statistic is to the value of the population parameters, the more representative the sample is to the population, and the more reliable the sample statistics are used to infer the population parameters. Therefore, standard error is an indicator of the reliability of statistical inference.

In addition, it should be noted that standard error can also refer to the standard deviation of statistics such as sample standard deviation and variance, not just the standard deviation of the sample mean.

There are two main reasons for the error:

First, the performance and parameter error of the component: the theoretical value of the design is based on the ideal component, while the actual device cannot achieve the ideal performance and parameters. Just as you can't measure a size without error with a ruler.

Second, the error generated by the measuring instrument: the measuring instrument will produce errors in the process of sampling and processing to display, especially for the sampling of data, and the error cannot be avoided by the sampling rate of the data at any high frequency.

There are many other factors that cause errors, such as power supply internal resistance, line loss, etc.

It is mainly divided into two, one is instrument error. The second is the reading error. The instrument error is mainly whether the telescope and the instrument are orthogonal to the main axis. Whether the stage is level or not. The erroneous reading is that the reading of the main dial is not read accurately with the scale of the vernier ruler.

The probability of a guaranteed confidence interval covering a parameter is expressed as p=(1-a) and is called the confidence coefficient or confidence level. In fracturing reliability engineering, the confidence coefficient is determined according to the interval estimation method, and the relationship between the upper confidence limit u and the lower confidence limit l.

For example, in the interval estimation that obeys the exponential distribution, the risk rate is 10%, that is, in the reliability test with a confidence level of 90%, if the number of faults is less than or equal to 1, the upper and lower confidence limits of the timing truncation are (,.

The three main factors that contribute to the error are:

There are certain limitations and deviations in human perception, judgment, operation, and measurement capabilities, which may lead to errors. For example, subjective judgments, inaccurate measurements, improper operation, or unskilled technology can all cause errors.

2.Instrument or equipment factors: The performance and accuracy of an instrument or equipment can also have an impact on the measurement results.

Factors such as the accuracy, stability, sensitivity, and calibration status of the instrument can all have an impact on the measurement results. If the instrument is inaccurate, aged, damaged, or not properly calibrated, it can lead to measurement errors.

3.Environmental factors: Environmental conditions also have an impact on the measurement results.

Environmental factors such as temperature, humidity, pressure, and light may affect the accuracy of the measurement. For example, temperature changes can cause the dimensions of an object to expand or contract, which in turn can cause measurement errors. In addition, noise, interference, vibration, etc. in the environment can also interfere with the measurement process, resulting in errors.

It should be noted that errors cannot be completely avoided, but the impact of errors can be reduced through appropriate measures and methods. For example, the use of more accurate instruments, accurate measurement methods, strict operating specifications, and appropriate environmental controls can effectively reduce the occurrence of errors. In addition to this source, multi-line repeated measurements, data analysis, and correction can also help hail bands identify and correct errors and improve the accuracy of measurements.

The causes of experimental error are human factors, gage factors, and force factors.

1. Human factors:

Errors caused by human factors, including misreading, miscalculation and parallax. Misreading often occurs in the liquid stare of measuring tools such as vernier rulers and centimeter cards, and the scale of vernier ruler is easy to cause misreading of a minimum reading. If it is often misread as mm or mm at mm, the centimeter card scale is easy to misread the size of a pitch, such as mm is often misread as mm or mm.

2. Measuring tool factors:

Errors caused by gage factors, including scale errors, wear errors, and uncorrected before use. Whether the scale division is accurate or not, must be corrected and traced by a more sophisticated instrument, and the measuring tool will produce a considerable degree of wear after a period of use, so it must be calibrated or sent for repair before it can be used again.

3. Strength factor:

Due to the contact force used in the measurement or the deflection error caused by the contact, according to Hooke's law, when measuring the size, if the measuring shaft is in contact with the machine with a certain measuring force, the shaft and the machine will produce local or comprehensive elastic deformation, in order to prevent this elastic deformation, the shaft and the machine should be made of the same material.

Ways to reduce the error:

1. Choose high-precision measurement tools

Use the standardized and scientific instruments manufactured by the designated manufacturers.

2. Average measurement for multiple measurements, etc

It is a method of taking multiple measurements of a certain invariant physical quantity in an experiment, and then adding up all the measured data and dividing it by the number of measurements to obtain the average value. The multi-measurement averaging method is a method often used to reduce measurement errors during experiments, and it should be noted that the average should be taken with the same number of decimal places as the number of decimal places of each measurement.

For example, when measuring the length, some students may be too large and some students may be small, so the average value can reduce the experimental error.