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How To Avoid The Texas Sharpshooter Fallacy In Data Analysis

February 23, 2017 at 11:51 AM

The Texas Sharpshooter Fallacy written on a red barn wall next to a target with bullet holes

The rise of Big Data, data science and predictive analytics to help solve real world problems is just an extension of science marching on. Science is humanity’s tool for better understanding the world. The tools that we use to build models, test hypotheses, look for trends to build value with our brand all derive directly from scientific principles.

With these principles comes a myriad of obstacles. The obstacles are known to philosophers as “logical fallacies”, which I outlined in my previous post "The 7 Logical Fallacies to avoid in Data Analysis."  In this blog post, we focus on the Texas Sharpshooter Fallacy and how to avoid it in your data analysis.

What it is the Texas Sharpshooter Fallacy?

This is a common mistake made by human beings. In essence, it is looking at a large amount of data, identifying small patterns and deriving a conclusion based on the patterns.

The name derives from a story of a Texan marksman who shoots a large amount of bullets at a barn door. He then finds the closest cluster of bullets and draws a target around them and thereby claims that he is a sharpshooter.

How to avoid the Texas Sharpshooter Fallacy

Post-hoc hunting of anomalies and patterns is commonplace in data analytics. There is no real problem with identifying patterns in data through an observational study, but this should result in a hypothesis and not a conclusion. Hypotheses should then be tested against another set of data. To extend our metaphor, the marksman should, after drawing his target, go back and take aim to see whether he can hit the target again.

This is partly why we use hold-out samples when we build models (e.g. we build a model on a random 80% of the population, but will then test the model against the 20% hold-out).

The reality is that all data will have anomalies and we can hunt for these, but we should not rest our conclusions based on these anomalies, we should rather test our hypotheses about the anomalies on hold-out samples, out-of-time tests or new tests.

This logical flaw is well known in applied physics and epidemiology. Certain studies known as “observational studies” may be conducted to look for anomalies in data. These anomalies may be presented, but a conclusion is not drawn as the independent variable is not controlled for. A follow-up study would be a randomised controlled trial to determine whether the results of the observational study could be replicated.

The Texas Sharpshooter fallacy is just one of many statistical pitfalls to avoid in data analysis. Read my post on the 7 Logical Fallacies to avoid in Data Analysis. I'll be covering each logical fallacy covered in my initial blog post on this topic - The 10 Logical Fallacies to avoid in Data Analysis - so make sure to subscribe to our blog to read the new posts in this series.
Truthseeker - logical fallacies

Thomas Maydon
Thomas Maydon
Thomas Maydon is the Head of Credit Solutions at Principa. With over 17 years of experience in the Southern African, West African and Middle Eastern retail credit markets, Tom has primarily been involved in consulting, analytics, credit bureau and predictive modelling services. He has experience in all aspects of the credit life cycle (in multiple industries) including intelligent prospecting, originations, strategy simulation, affordability analysis, behavioural modelling, pricing analysis, collections processes, and provisions (including Basel II) and profitability calculations.

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  It is common knowledge in the industry that the credit risk assessment of a consumer applying for credit is far less complex than that of a business that is applying for credit. Why is this the case? Simply put, consumers are usually very similar in their requirements and risks (homogenous) whilst businesses have far more varying risk elements (heterogenous). In this blog we will look at all the different risk elements within a business (here SME) credit application. These are: Risk of proprietors Risk of business Reason for loan Financial ratios Size of loan Risk industry Risk of region Before we delve into this list, it is worth noting that all of these factors need to be deployable as assessment tools within your originations system so it is key that you ensure your system can manage them. If you are on the look out for a loans origination system, then look no further than Principa’s AppSmart. If you are looking for a decision engine to manage your scorecards, policy rules and terms of business then take a look at our DecisionSmart business rules engine. AppSmart and DecisionSmart are part of Principa’s FinSmart Universe allowing for effective credit management across the customer life-cycle.  The different risk elements within a business credit application 1) Risk of proprietors For smaller organisations the risk of the business is inextricably linked to the financial well-being of the proprietors. How small is small? The rule of thumb is companies with up to two to three proprietors should have their proprietors assessed for risk too. This fits in with the SME segment. What data should be looked at? Generally in countries with mature credit bureaux, credit data is looked at including the score (there is normally a score cut-off) and then negative information such as the existence of judgements or defaults; these are typically used within policy rules. Those businesses with proprietors with excessive numbers of “negatives” may be disqualified from the loan application. Some credit bureaux offer a score of an individual based on the performance of all the businesses with which they are associated. This can also be useful in the credit risk assessment process. Another innovation being adopted internationally is the use of psychometrics in credit evaluation of the proprietors. To find out more about adopting credit scoring, read our blog on how to adopt credit scoring.   2) Risk of business The risk of the business should be managed through both scores and policy rules. Lenders will look at information such as the age of company, the experience of directors and the size of company etc. within a score. Alternatively, many lenders utilise the business score offered by credit bureaux. These scores are typically not as strong as consumer scores as the underlying data is limited and sometimes problematic. For example, large successful organisations may have judgements registered against their name which, unlike for consumers, is not necessarily a direct indication of the inability to service debt.   3) Reason for loan The reason for a loan is used more widely in business lending as opposed to unsecured consumer lending. Venture capital, working capital, invoice discounting and bridging finance are just some of many types of loan/facilities available and lenders need to equip themselves with the ability to manage each of these customer types whether it is within originations or collections. Prudent lenders venturing into the SME space for the first time often focus on one or two of these loan types and then expand later – as the operational implication for each type of loan is complex. 4) Financial ratios Financial ratios are core to commercial credit risk assessment. The main challenge here is to ensure that reliable financials are available from the customer. 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Coverage ratios are worked out considering the loan/facility that is being applied for. Finally, liquidity ratios indicate the ability for a company to convert its assets into cash. There are a variety of ratios used here. The current ratio is simply the ratio of assets to liabilities. The quick ratio is the ability for the business to pay its current debts off with readily available assets. The higher the liquidity ratios the better. Ratios are used both within credit scorecards as well as within policy rules. You can read more about these ratios here. 5) Size of loan When assessing credit risk for a consumer, the risk of the consumer does not normally change with the change of loan amount or facility (subject to the consumer passing affordability criteria). With business loans, loan amounts can range quite dramatically, and the risk of the applicant is normally tied to the loan amount requested. 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In the graph below the performance of an industry is tracked for two years and then projected over the next 6 months; this is then compared to the country’s GDP. As the industry appears to track above the projected GDP, a positive outlook is given to this applicant and this may affect them favourably in the credit application.                   7) Risk of Region   The last area of assessment is risk of region. Of the seven, this one is used the least. Here businesses,  either on book or on the bureau, are assessed against their geo-code. Each geo-code is clustered, and the projected outlook is given as positive, static or negative. As with industry this can be used within the assessment process as a policy rule or within a scorecard.   Bringing the seven risk categories together in a risk assessment These seven risk assessment categories are all important in the risk assessment process. How you bring it all together is critical. If you would like to discuss your SME evaluation challenges or find out more about what we offer in credit management software (like AppSmart and DecisionSmart), get in touch with us here.

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