Language
Country/Area
No result found
Reality of measurement: How do you tell if your data is reliable?
In today's data-driven society, data accuracy and reliability are increasingly important. Whether in scientific research, engineering applications or business decision-making, how to ensure the authenticity of the data used is a key issue.
Accuracy is how close a measurement is to its true value, while precision is how close measurements are to each other.
Although accuracy and precision can be used interchangeably, they are clearly distinguished in the context of exact sciences. Accuracy usually refers to systematic error - that is, the deviation in measurement, while precision describes the statistical variation and refers to the repeatability of the data.
According to the International Organization for Standardization (ISO), accuracy is described as how close a measurement is to the true value, involving a combination of random and systematic errors. The so-called "realism" refers to the closeness between the average of a set of measurement results and the actual value.
A measurement system is considered valid if it possesses both accuracy and precision.
In industrial instrumentation, accuracy is often used to express measurement tolerance, which defines the range of error in measurements made under normal operating conditions. An ideal measuring device should have accuracy and precision, and the data should be close to and closely concentrated on the true value.
In statistics, the concepts of accuracy and error are important. If a measurement system has systematic errors, increasing the sample size will generally improve precision but not accuracy. This means that even if the measurements are consistent, they will still be inaccurate if there is a deviation.
Precision and accuracy are not interchangeable; a set of data can be accurate but not precise, accurate but not accurate, or neither.
In routine data processing and evaluation, a frequently used technique involves repeated measurement of a traceable standard to establish the accuracy and precision of the measurement process. These standards are defined by the International System of Units (SI) to ensure consistency and reliability in all measurements around the world.
Going further, the accuracy of measurement also involves the process of correcting deviations. For example, in commercial applications, if a system can provide accurate results but does not account for bias in its calculations, the validity of the system will be questioned.
In psychometry and psychophysics, accuracy and validity are used interchangeably, while precision is considered a manifestation of reliability. These concepts help researchers determine the validity of measurement tools and the consistency of results.
In data retrieval systems, accuracy and precision are important indicators for evaluating the effectiveness of data acquisition.
Finally, we often find that accuracy and precision can mean different things in different data contexts. In some cases, such as classification systems in machine learning, accuracy is used as a metric to assess the model's ability to correctly identify or rule out a condition. Here, there are certain quantitative methods for accuracy to evaluate the effectiveness and reliability of the model.
As technology advances, so do our data measurement and analysis tools. But most importantly, as data users, how can we ensure the authenticity of the data we obtain, and when using this data to make decisions, how can we avoid making wrong choices due to misunderstandings about accuracy and precision?
