As a domain expert, I'd like to clarify that errors in experimental settings are indeed categorized into three primary types: systematic errors, random errors, and blunders. Each type has its own characteristics and implications for the accuracy and reliability of experimental results. Let's delve into each one in detail.

Systematic Errors
Systematic errors, also known as systematic biases, are predictable and reproducible errors that affect the results of an experiment in a consistent manner. These errors can arise from various sources, including faulty equipment, incorrect calibration, or flawed experimental design. The key feature of systematic errors is that they consistently push the measured values in one direction—either too high or too low—relative to the true value. For instance, if a thermometer is consistently off by a certain degree, it will always over- or underestimate the temperature, leading to a systematic error.

Systematic errors can be further classified into several subtypes based on their source:
1. Instrumental Errors: These occur due to the limitations or imperfections of the measuring instruments.
2. Environmental Errors: These arise from changes in the environment that affect the experiment, such as temperature fluctuations or electromagnetic interference.
3. Human Errors: These are mistakes made by the experimenter, which can include incorrect operation of equipment or misinterpretation of results.

Random Errors
Random errors, also referred to as random variations or random fluctuations, are unpredictable and occur due to chance. They are inherent in any measurement process and are the result of a multitude of small, uncontrolled factors that cannot be easily identified or controlled. Random errors cause the measured values to deviate from the true value in a random manner, with no consistent direction. They are typically distributed in a normal distribution pattern around the true value.

The impact of random errors can be minimized but not completely eliminated. Techniques such as increasing the number of measurements, using more precise instruments, and employing statistical methods can help to reduce the influence of random errors on the final results.

Blunders
Blunders, also known as mistakes or gross errors, are the result of human negligence or carelessness. They are not inherent to the measurement process but are instead due to the experimenter's errors. Blunders can be quite significant and are often easily identifiable. They can occur at any stage of the experimental process, from setting up the experiment to recording and analyzing the data.

Examples of blunders include:
1. Recording incorrect data due to a simple oversight.
2. Using the wrong reagents in a chemical experiment.
3. Failing to follow the experimental protocol correctly.

Blunders are distinct from random errors because they are not part of the natural variability in measurements but are instead avoidable mistakes.

In summary, understanding and managing these three types of errors is crucial for the integrity and validity of experimental results. Systematic errors require careful calibration and control, random errors can be mitigated through statistical analysis and methodological improvements, and blunders must be avoided through careful attention to detail and adherence to experimental protocols.

Now, let's translate this into Chinese.

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TYPES OF EXPERIMENTAL. Errors are normally classified in three categories: systematic errors, random errors, and blunders. Systematic errors are due to identified causes and can, in principle, be eliminated. Errors of this type result in measured values that are consistently too high or consistently too low.read more >>