12 Types of Errors in Business Analysis

When businesses, organizations, and individuals collect and analyze data, they often need to know the accuracy of their statistics. 

Knowing statistical inaccuracy, also known as error, can help gauge the reliability of a statistic at a given percentage as well as how often it is likely to be accurate at that level. Errors in business analysis are important things you should know and is one sign of inefficient business.

Classifying the types of errors you may encounter in your own measurements can help you make better decisions and predictions in your own work as well.

In this article, we explain why it's important to quantify errors and provide a list of error types to help support your own business data analysis.

Types of Errors in Business Analysis

Why should you know how to calculate the error?

Knowing how to quantify errors can help you make informed decisions at work, especially in careers that rely on the collection and analysis of business data. 

Mathematicians, statisticians, engineers, and other technical professionals must often account for errors when they survey a population, develop processes, or sell products.

Calculation error can help you determine the precision, or consistency, of a measurement and its accuracy, or closeness, to the true value if any. It can help you make important business decisions for your organization.

Measuring the level of confidence you can have in certain measurements can also help you present your findings to colleagues and company leaders. Calculating errors can also help explain missing information or gaps in your data.

If you want to become a data analyst, you can find out some of the skills you have to learn through this article.

12 types of errors in business data analysis

The types of errors that analysts encounter in their work can often be useful in other areas such as business as well. 

To better understand how data can vary in precision and accuracy, here are 12 common types of errors you might encounter in your analysis, many of which are common in the sciences:

1. Random error

Random errors have to do with the limitations of the tools or mechanisms you use to collect data. This type of error is usually unpredictable and can be higher or lower than the actual value. If random error interferes with your data analysis, a larger sample size can help.

2. Systematic error

Systematic error is a type of error that occurs consistently in the same direction, either low or high. They can be difficult to distinguish from accurate data because they may follow the same pattern.

While it is possible to adjust your sampling method to account for systematic error, changing the sample size will not account for this type of error due to its consistency.

3. Calibration factor

Calibration error relates to the tool you used to measure your data set. The accuracy of your own measuring instrument can cause errors in your results. Calibration error rates are usually more significant at larger scales because they often reflect the percentage of the sample as a whole.

4. Environmental factors

Physical elements of the environment can lead to wrong results when studying something real. Adverse weather conditions, for example, can increase the margin of error when you measure the popularity of food trucks on the coast. 

Ambient room temperature, in other instances, can affect the potential for error in studies of animal activity levels.

5. Instrument resolution method

The tools you use to measure physical goods can also limit the accuracy of your data. For example, if your scale only measures to the nearest ounce, you won't be able to get more granular than a given unit. 

In addition, the accuracy of less literal results such as surveys and focus groups may be limited by the specifics of your scale or measuring instrument.

 6. Physical variations

Differences in the physical characteristics of the items you measure may contribute to your margin of error. 

Carefully identifying variation between individuals in a sample pool can shed light on possible measured outliers—for example, very unusual wind speeds during a weather monitoring period.

7. Too many variables

When studying data to measure a particular trend or phenomenon, it is important to keep every variable consistent except the one you want to study. For example, if you are examining the impact of one ad on your customer base, you need to negate the impact of any other ads they might see.

Ignoring variables that might influence your results can lead to inaccurate results. Sometimes you can prevent this by working with a colleague to brainstorm each factor that might influence your results and keep each variable consistent throughout your study.

8. Zero offset

This error occurs when measuring a physical quantity using a scale or similar device. If your gauge is not set to zero before you start measuring, your results will drop to the same level as your blank from zero.

For example, if you weighed vegetables and your scale read 0.02 kg before you placed the vegetables on the scale, your final measurement will also be 0.02 kg.

9. Parallax

Parallax is the word for the way your perspective changes depending on how you observe something—both point of view and use of a device like a telescope, where that's relevant. 

Sometimes parallax can cause data reporting errors, especially when using analogue instruments because they often rely on the accuracy of the human eye.

10.Drift instrument

Sometimes, gauges can become less accurate with age. Paying attention to instrument conditions can be a good way to anticipate and correct instrument deviations before increasing the margin of error in a data set.

11. Time lag/hysteresis

Some devices, such as thermometers, must be calibrated to ambient conditions, such as temperature, before they can take an accurate reading. Measuring with such a device before it has had time to acclimate to its environment can result in errors.

Similarly, hysteresis is when the gauge lags behind its own reading. In the thermometer example, for example, taking a reading before the thermometer reaches its actual temperature would be a hysteresis error.

12. Human error

This kind of error occurs as a result of a researcher's mistake or oversight. Incorrect measurements or inadvertent technique, for example, can result in this kind of error. Unrecognized bias can also contribute to this kind of statistical error. 

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