science

Noisy data

Noisy data is a science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Noisy data rather than just read about it. In short: Noisy data are data that are corrupted, distorted, or have a low signal-to-noise ratio. Improper procedures (or improperly documented procedures) to subtract out the noise in data can lead to a false sense of accuracy or false conclusions.

Key takeaways

Noisy data belongs to science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Noisy data to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Noisy data from memory before moving on to harder problems.

Reference excerpt

Noisy data are data that are corrupted, distorted, or have a low signal-to-noise ratio. Improper procedures (or improperly documented procedures) to subtract out the noise in data can lead to a false sense of accuracy or false conclusions. Noisy data are data with a large amount of additional meaningless information in them, known as noise. This includes data corruption and the term is often used as a synonym for corrupt data. It also includes any data that a user system cannot understand and interpret correctly. Many systems, for example, cannot use unstructured text. Noisy data can adversely affect the results of any data analysis and skew conclusions if not handled properly. Statistical analysis is sometimes used to weed the noise out of noisy data.

Sources of noise

Differences in real-world measured data from the true values come about from multiple factors affecting the measurement. Random noise is often a large component of the noise in data. Random noise in a signal is quantified as the signal-to-noise ratio. Random noise contains a wide range of frequencies, and is also called white noise (as wide range of colors of light combine to make white). Random noise affects the data collection and data preparation processes, where errors commonly occur. Noise has two main sources: errors introduced by measurement tools and random errors introduced by processing or by experts when the data is gathered. Improper filtering can add noise if the filtered signal is treated as if it were a directly measured signal. As an example, Convolution-type digital filters such a moving average can have side effects such as lags or truncation of peaks. Differentiating digital filters amplifies random noise in the original data. Outlier data are data that appear to not belong in the data set. It can be caused by human error such as transposing numerals, mislabeling, programming bugs, etc. If actual outliers are not removed from the data set, they corrupt the results to a small or large degree, depending on circumstances. If valid data is identified as an outlier and is mistakenly removed, that also corrupts results. Individuals may deliberately skew data to influence the results toward a desired conclusion. Data that looks good with few outliers reflects well on the individual collecting it, and so there may be incentive to remove more data as outliers or make the data look smoother than it is.

References

Illustrations

Worked examples

Example 1 — a first encounter with Noisy data

Start with the simplest possible case. Write down what Noisy data claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Noisy data before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Noisy data ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Noisy data

In research: Noisy data appears in science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Noisy data in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Noisy data is common in secondary-school and first-year university syllabi. It links to neighbouring topics Digital audio, Noise, so understanding it makes those chapters shorter.
In everyday life: Look for Noisy data outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Noisy data in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Noisy data means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Noisy data out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Noisy data in simple terms?

Why does Noisy data matter?

Because it connects several science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Noisy data?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Noisy data.