This article discusses the identification and handling of error sources in experimental analysis, including systematic, random, and operational errors. This paper elaborates on how statistical methods and precise measurement techniques can be used to improve the accuracy and reliability of experimental results. The article also discusses the use of significant figures and their importance to the interpretation of results, providing comprehensive guidance for data analysis in scientific research.
error
Definition and importance of error
Error refers to the difference between the experimental result and the true value, and this deviation can occur due to a variety of factors. The presence of errors is an unavoidable part of the experimental process and can reveal the inaccuracy of the measurements.
Errors directly affect the reliability of the analysis results and the accuracy of the experiment. If errors are not properly addressed or considered, they can lead to erroneous conclusions or unreliable data. Therefore, understanding and controlling errors is essential to ensure the validity and accuracy of experimental results.
Classification of errors
systematic error
Systematic error is caused by fixed causes, and it has a certain regularity. This type of error usually affects every measurement of the experiment, and the direction and magnitude of the deviation is the same in different experiments.
Cause:
Method errors – caused by inherent flaws or limitations in the experimental method itself. For example, some methods may not be applicable under certain conditions, leading to systematic biases.
Instrument Error – Problems with the accuracy of the instrument itself, or defects in the instrument's manufacturing process, can lead to inaccurate measurements. For example, inaccurate calibration of the instrument or errors caused by aging.
Reagent Error – The reagents used may be impure or contain impurities, which can affect the reaction results, introducing errors.
Operational error - the error caused by improper methods and non-standard operation in the operation process of the experimenter. For example, inaccurate weighing or incomplete liquid transfer.
Personal error – Error due to subjective judgment or habitual operational differences of the observer. Different observers may perceive the same phenomenon differently.
Environmental error - the experimental environmental conditions do not meet the requirements, such as temperature, humidity, air pressure and other environmental factors have an impact on the experimental results.
random error
Random errors are caused by accidental and unexpected factors and have statistical regularity. This type of error is unpredictable and manifests itself as fluctuations in the measured value.
Characteristics: Random error is expressed as the dispersion of measurement results, which usually obeys a normal distribution. The effects of such errors are often accidental and cannot be predicted with a single measurement.
Reduction Methods – Reduce the impact of random errors by repeating measurements and averaging them. The data measured over multiple times can be averaged to reduce the influence of individual chance factors, resulting in more reliable results.
Poor negligence
Faulty errors are significant errors caused by improper operation or carelessness. This type of error is usually caused by the experimenter's error or negligence.
Characteristics: Fault errors usually do not have a fixed regularity, and are manifested as occasional large deviations. Such errors can be avoided with more detailed and standardized operations. Its presence means that some aspects of the experimental process require strict attention to prevent significant errors from being introduced due to carelessness or improper handling.
Error representation and data processing
How the error is noted
Truth – A theoretically reliable true value that represents the target value measured experimentally. Although theoretically certain, it is not fully available in practice.
Arithmetic mean – the average value obtained by adding up multiple measurements and dividing them by the number of measurements, is used to represent the central trend of the data, reflecting the overall level of the measurements.
Median – The value in the middle of the middle after sorting the data by size, which is not affected by extreme values and is a better representation of the central trend of the data.
Repeatability – the degree of proximity between the results of multiple measurements under the same experimental conditions, assessing the stability and consistency of the experiment.
Reproducibility – The proximity of measurement results under different experimental conditions (e.g., different experimenters, different times, different instruments) to assess the repeatability and reliability of experimental results.
accuracy
The degree of agreement between the experimental measured value and the true value reflects the accuracy of the measurement. High accuracy means that there is little difference between the measurement result and the true value.
Accuracy is assessed by calculating the magnitude of the error. The smaller the error, the closer the measurement result is to the true value and the higher the accuracy.
Data processing and analysis
Error analysis
Identify the various factors that contribute to errors, including systematic, random, and negligent errors, so that you can make targeted improvements.
By optimizing the experimental design, improving the instrument calibration, standardizing the operation process, etc., the error is reduced and the accuracy of the experimental data is improved.
Significant figures
Significant figures indicate the accuracy of the data and ensure the accuracy of the data report. Significant figures include all definite digits as well as an indefinite last digit.
In data processing and result reporting, the results are presented according to the rules of significant figures to ensure the accuracy and reliability of the data expressed.
summary
The key to experimental analysis is to identify and correct errors to ensure the reliability of the data. The accuracy of experimental results can be effectively improved through reasonable statistical methods and accurate measurement techniques. In addition, understanding the concept of significant figures helps to interpret experimental data more accurately. Comprehensive error analysis not only improves the quality of the data, but also enhances the scientific nature of the research conclusions.
