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Good Data Analysis  |  ML Universal Guides  |  Google Developers

Good Data Analysis  |  ML Universal Guides  |  Google Developers

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Not only do we typically work with very large data sets, but those data sets are extremely rich. That is, each row of data typically has many, many attributes. When you combine this with the temporal sequences of events for a given user, there are an enormous number of ways of looking at the data...

Most practitioners use summary metrics (for example, mean, median, standard deviation, and so on) to communicate about distributions.

However, you should usually examine much richer distribution representations by generating histograms, cumulative distribution functions (CDFs), Quantile-Q...

Examine outliers carefully because they can be canaries in the coal mine that indicate more fundamental problems with your analysis.

It's fine to exclude outliers from your data or to lump them together into an "unusual" category, but you should make sure that you know why data ended up i...

Randomness exists and will fool us. Some people think, “Google has so much data; the noise goes away.” This simply isn’t true. Every number or summary of data that you produce should have an accompanying notion of your confidence in this estimate (through measures such as confidence intervals and...

Anytime you are producing new analysis code, you need to look at examples from the underlying data and how your code is interpreting those examples. Your analysis is abstracting away many details from the underlying data to produce useful summaries.

How you sample these examples is importa...

Slicing means separating your data into subgroups and looking at metric values for each subgroup separately. We commonly slice along dimensions like browser, locale, domain, device type, and so on. If the underlying phenomenon is likely to work differently across subgroups, you must slice the dat...

With a large volume of data, it can be tempting to focus solely on statistical significance or to home in on the details of every bit of data. But you need to ask yourself, "Even if it is true that value X is 0.1% more than value Y, does it matter?"

This can be especially important if you...

You should almost always try slicing data by units of time because many disturbances to underlying data happen as our systems evolve over time. (We often use days, but other units of time may also be useful.)

During the initial launch of a feature or new data collection, practitioners ofte...

Looking at day-over-day data also gives you a sense of the variation in the data that would eventually lead to confidence intervals or claims of statistical significance. This should not generally replace rigorous confidence-interval calculation, but often with large changes you can see they will...

Almost every large data analysis starts by filtering data in various stages. Maybe you want to consider only US users, or web searches, or searches with ads. Whatever the case, you must:

  • Acknowledge and clearly specify what filtering you are doing.
  • Count the amount of data b...

The most interesting metrics are ratios of underlying measures. Oftentimes, interesting filtering or other data choices are hidden in the precise definitions of the numerator and denominator. For example, which of the following does “Queries / User” actually mean?

  • Queries / Users wit...

Validation: Do I believe the data is self-consistent, that it was collected correctly, and that it represents what I think it does?

Description: What's the objective interpretation of this data? For example, "Users make fewer queries classified as X," "In t...

Before looking at any data, make sure you understand the context in which the data was collected. If the data comes from an experiment, look at the configuration of the experiment. If it's from new client instrumentation, make sure you have at least a rough understanding of how the data is collec...

As part of the "Validation" stage, before actually answering the question you are interested in (for example, "Did adding a picture of a face increase or decrease clicks?"), rule out any other variability in the data that might affect the experiment. For example:

  • Did the number of us...

When looking at new features and new data, it's particularly tempting to jump right into the metrics that are new or special for this new feature. However, you should always look at standard metrics first, even if you expect them to change.

For example, when adding a new universal block t...

Especially if you are trying to capture a new phenomenon, try to measure the same underlying thing in multiple ways. Then, determine whether these multiple measurements are consistent.

By using multiple measurements, you can identify bugs in measurement or logging code, unexpected feature...

Both slicing and consistency over time are particular examples of checking for reproducibility. If a phenomenon is important and meaningful, you should see it across different user populations and time. But verifying reproducibility means more than performing these two checks. If you are building...

Often you will be calculating a metric that is similar to things that have been counted in the past. You should compare your metrics to metrics reported in the past, even if these measurements are on different user populations.

You do not need to get an exact agreement, but you should be i...

If you create new metrics (possibly by gathering a novel data source) and try to learn something new, you won’t know if your new metric is right. With new metrics, you should first apply them to a known feature or data.

If you have a new metric for where users are directing their attention...

Typically, data analysis for a complex problem is iterative. You will discover anomalies, trends, or other features of the data. Naturally, you will develop theories to explain this data. Don’t just develop a theory and proclaim it to be true. Look for evidence (inside or outside the data) to con...

Good data analysis will have a story to tell. To make sure it’s the right story, you need to tell the story to yourself, then look for evidence that it’s wrong. One way of doing this is to ask yourself, “What experiments would I run that would validate/invalidate the story I am telling?” Even if ...

When doing exploratory analysis, perform as many iterations of the whole analysis as possible. Typically you will have multiple steps of signal gathering, processing, modeling, etc. If you spend too long getting the very first stage of your initial signals perfect, you are missing out on opportun...

We typically define various metrics around user success.

You can not use the metric that is fed back to your system as a basis for evaluating your change. If you show more ads that get more clicks, you can not use “more clicks” as a basis for deciding that users are happier, even though “m...

  • As you work with data, you must become both the champion of the insights you are gaining and a skeptic of them. You will hopefully find some interesting phenomena in the data you look at. When you detect an interesting phenomenon, ask yourself the following questions:
  • What other data...

There’s always a motivation to analyze data. Formulating your needs as questions or hypotheses helps ensure that you are gathering the data you should be gathering and that you are thinking about the possible gaps in the data. Of course, the questions you ask should evolve as you look at the data...

When making theories about data, we often want to assert that "X causes Y"—for example, "the page getting slower caused users to click less." You can not simply establish causation because of correlation. By considering how you would validate a theory of causation, you can usually develop a good ...

The previous points suggested some ways to get yourself to do the right kinds of soundness checking and validation. But sharing with a peer is one of the best ways to force yourself to do all these things. A skilled peer can provide qualitatively different feedback than the consumers of your data...

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