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A common problem with data provided as a spreadsheet is that the first few rows of data will actually be descriptions or notes about the data rather than column headings or data itself. A key or data dictionary may also be placed in the middle of the spreadsheet.

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Header rows may be repeated. Or the spreadsheet will include multiple tables which may have different column headings one after the other in the same sheet rather than separated into different sheets. In all of these cases the main solution is simply to identify the problem.

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Obviously trying to perform any analysis on a spreadsheet that has these kinds of problems will fail, sometimes for non-obvious reasons. When looking at new data for the first time it's always a good idea to ensure there aren't extra header rows or other formatting characters inserted amongst the data. Imagine a dataset with rows and a column called cost. In 50 of the rows the cost column is blank.

What is the average of that column? There is no one definitive answer. In general, if you're going to compute aggregates on columns that are missing data, you can safely do so by filtering out the missing rows first, but be careful not to compare aggregates from two different columns where different rows were missing values! In some cases the missing values might also be legitimately interpreted as 0. If you're not sure, ask an expert or just don't do it. This is an error you can make in your analysis, but it's also an error that others can make and pass on to you, so watch out for it if data comes to you with aggregates already computed.

A non-random sampling error occurs when a survey or other sampled dataset either intentionally or accidentally fails to cover the entire population. This can happen for a variety of reasons ranging from time-of-day to the respondent's native language and is a common source of error in sociological research. It can also happen for less obvious reasons, such as when a researcher thinks they have a complete dataset and chooses to work with only part of it. If the original dataset was incomplete for any reason then any conclusions drawn from their sample will be incorrect.

The only thing you can do to fix a non-random sample is avoid using that data. I know of no other single issue that causes more reporting errors than the unreflective usage of numbers with very large margins-of-error. MOE is usually associated with survey data. The MOE is a measure of the range of possible true values. The smaller the relevant population, the larger the MOE will be. The first two numbers are safe to report. The third number should never be used in published reporting. Sometimes the problem isn't that the margin of error is too large , it's that nobody ever bothered to figure out what it was in the first place.

This is one problem with unscientific polls. Without computing a MOE, it is impossible to know how accurate the results are. As a general rule, anytime you have data that are from a survey you should ask for what the MOE is. If the source can't tell you, those data probably aren't worth using for any serious analysis. Like a sample that is not random , a biased sample results from a lack of care with how the sampling is executed.

Or, from willfully misrepresenting it. A sample might be biased because it was conducted on the internet and poorer people don't use the internet as frequently as the rich. Surveys must be carefully weighted to ensure they cover proportional segments of any population that could skew the results. It's almost impossible to do this perfectly so it is often done wrong. Manual editing is almost the same problem as data being entered by humans except that it happens after the fact. In fact, data are often manually edited in an attempt to fix data that were originally entered by humans.

Problems start to creep in when the person doing the editing doesn't have complete knowledge of the original data. I once saw someone spontaneously "correct" a name in a dataset from Smit to Smith. Was that person's name really Smith? I don't know, but I do know that value is now a problem.

Without a record of that change, it's impossible to verify what it should be. Issues with manual editing are one reason why you always want to ensure your data have well-documented provenance. A lack of provenance can be a good indication that someone may have monkeyed with it. Academics and policy analysts often get data from the government, monkey with them and then redistribute them to journalists. Without any record of their changes it's impossible to know if the changes they made were justified. Whenever feasible always try to get the primary source or at least the earliest version you can and then do your own analysis from that.

Currency inflation means that over time money changes in value. There is no way to tell if numbers have been "inflation adjusted" just by looking at them. If you get data and you aren't sure if they have been adjusted then check with your source. If they haven't you'll likely want to perform the adjustment. This inflation adjuster is a good place to start. Many types of data fluctuate naturally due to some underlying forces. The best known example of this is employment fluctuating with the seasons. Economists have developed a variety of methods of compensating for this variation.

The details of those methods aren't particularly important, but it is important that you know if the data you're using have been "seasonally adjusted". If they haven't and you want to compare employment from month to month you will probably want to get adjusted data from your source. Adjusting them yourself is much harder than with inflation. A source can accidentally or intentionally misrepresent the world by giving you data that stops or starts at a specific time. For a potent example see 's widely reported "national crime wave".

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There was no crime wave. What there was was a series of spikes in particular cities when compared to just the last few years. Had journalists examined a wider timeframe they would have seen that violent crime was higher virtually everywhere in the US ten years before. And twenty years before it was nearly double. If you have data that covers a limited timeframe try to avoid starting your calculations with the very first time period you have data for.

If you start a few years or months or days into the data you can have confidence that you aren't making a comparison which would be invalidated by having a single additional data point. Crime statistics are often manipulated for political purposes by comparing to a year when crime was very high.

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In either of these cases, may or may not be an appropriate year for comparison. It could have been an unusually high crime year. This also happens when comparing places. If I want to make one country look bad, I simply express the data about it relative to whichever country is doing the best.

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This problem tends to crop up in subjects where people have a strong confirmation bias. And whatever you do, don't use this technique yourself to make a point you think is important. That's inexcusable. Sometimes the only data you have are from a source you would rather not rely on. In some situations that's just fine.

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The only people who know how many guns are made are gun manufacturers. However, if you have data from a questionable maker always check it with another expert. Better yet, check it with two or three. Don't publish data from a biased source unless you have substantial corroborating evidence.

It's very easy for false assumptions, errors or outright falsehoods to be introduced into these data collection processes. For this reason it's important that methods used be transparent. It's rare that you'll know exactly how a dataset was gathered, but indications of a problem can include numbers that assert unrealistic precision and data that are too good to be true.

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Sometimes the origin story may just be fishy: did such-and-such academic really interview 50 active gang members from the south side of Chicago? If the way the data were gathered seems questionable and your source can't offer you ironclad provenance then you should always verify with another expert that the data could reasonably have been collected in the way that was described. Outside of hard science, few things are routinely measured with more than two decimal places of accuracy. If a dataset lands on your desk that purports to show a factory's emissions to the 7th decimal place that is a dead giveaway that it was estimated from other values.

That in and of itself may not be a problem, but it's important to be transparent about estimates. They are often wrong. I recently created a dataset of how long it takes for messages to reach different destinations over the internet. All of the times were in the range from 0. The other three were all over 5, seconds. This is a major red flag that something has gone wrong in the production of the data. In this particular case an error in the code I wrote caused some failures to continue counting while all other messages were being sent and received.

Outliers such as these can dramatically screw up your statistics—especially if you're using averages. You should probably be using medians. Whenever you have a new dataset it is a good idea to take a look at the largest and smallest values and ensure they are in a reasonable range. If the data justifies it you may also want to do a more statistically rigorous analysis using standard deviations or median deviations. As a side-benefit of doing this work, outliers are often a great way to find story leads.

If there really was one country where it took 5, times as long to send a message over the internet, that would be a great story. Analysts who want to follow the trend of an issue often create indices of various values to track progress. There is nothing intrinsically wrong with using an index. They can have great explanatory power. However, it's important to be cautious of indices that combine disparate measures. For example, the United Nations Gender Inequality Index combines several measures related to women's progress toward equality.

One of the measures used in the GII is "representation of women in parliament". Two countries in the world have laws mandating gender representation in their parliaments: China and Pakistan. As a result these two countries perform far better in the index than countries that are similar in all other ways. Is this fair? It doesn't really matter, because it is confusing to anyone who doesn't know about this factor.

The GII and similar indices should always be used with careful analysis to ensure their underlying variables don't swing the index in unexpected ways. P-hacking is intentionally altering the data, changing the statistical analysis, or selectively reporting results in order to have statistically significant findings. Examples of this include: stop collecting data once you have a significant result, remove observations to get a significant result, or perform many analyses and only report the few that are significant. There has been some good reporting on this problem.

If you're going to publish the results of a study you need to understand what the p-value is, what that means and then make an educated decision about whether the results are worth using. Lots and lots of garbage study results make it into major publications because journalists don't understand p-values. Benford's Law is a theory which states that small digits 1, 2, 3 appear at the beginning of numbers much more frequently than large digits 7, 8, 9.

In theory Benford's Law can be used to detect anomalies in accounting practices or election results, though in practice it can easily be misapplied. If you suspect a dataset has been created or modified to deceive, Benford's Law is an excellent first test, but you should always verify your results with an expert before concluding your data have been manipulated. There is no global dataset of public opinion.

Bad Data Handbook (Cleaning Up The Data So You Can Get Back To Work)

Nobody knows the exact number of people living in Siberia. Crime statistics aren't comparable across borders. The US government is not going to tell you how much fissile material it keeps on hand. Beware any data that purport to represent something that you could not possibly know. It's not data.

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More advanced programmers will learn to use the open-source software available and how to best apply it to data in a business setting. Big Data requires an environment that supports a data-driven mentality. Using real-world examples Phil Simon tries to explain the economics behind Big Data.

To reap valuable insight from Big Data analytics, he claims, one does not need to be guru data scientist. Simply accepting that Big Data can give insight is in many cases enough to change the culture into an embracing one. Nate Silver is a renowned statistician and writer.

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Crucially, it not only discusses the practices of effective data mining, but how to convert findings into real business value. Drawing real-world examples from big business, this book is essential for any company wondering if a data scientist would actually be worth his first pay cheque.

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In the Europe of , it was a widely-accepted, scientific fact that all swans were white. In Australia, , the cygnus atratus, or black swan, was discovered.