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The 10 Logical Data Fallacies To Avoid In Data Analysis

February 15, 2017 at 3:17 PM

digital-thinker.png“Lies, damned lies and statistics” is the frequently quoted adage attributed to former British Prime Minister Benjamin Disraeli. The manipulation of data to fit a narrative is a very common occurrence from politics, economics to business and beyond. 

In this blog post, we'll touch on the more common logical fallacies that can be encountered and should be avoided in data analysis.

Logical fallacies in data interpretation

Statistics are simply numbers – how we (choose to) interpret them is up to us mere mortals and the key mental tool of critical thinking. It’s time to think about thinking.

A core competency of a data scientist is to be able to translate effects and patterns of data into real-life context. For business managers who need to periodically analyse data, an essential skill is ensuring that an interpretation is indeed correct and not tarnished by one of the many logical fallacies or mental mistakes that we all make in everyday life.

Philosophers have for some time understood the mistakes we make in reasoning. Aristotle is the first known philosopher to have established a list of logical fallacies. These fallacies are common mistakes made in arguing and thinking. An awareness of these is extremely helpful in sharpening one’s analytical ability. Developing awareness of thinking is known as metacognition and it is a key component to critical thinking. 

When it comes to analysing data or assessing a conclusion from the data or models presented, it is worthwhile to be aware of the reliability of what is presented. The reliability may vary due to the

Below is a list of the more common logical data fallacies to avoid in analytics - I'll by covering some of these in more detail in future blogs:

  1. The Cherry-picking fallacy of selectively choosing your data or statistics to prove your argument, or using confirmation bias and motivated reasoning instead of deductive reasoning in your analysis;
  2. The Texas Sharpshooter fallacy of looking for patterns, but ignoring contradictions;
  3. Correlation does not imply causation, or similarities between two statistics or trends does not imply that the one caused the other. Here are some funny examples of this;
  4. The Gambler's fallacy of looking at an unlikely string of events and implying that it will break; 
  5. The Hot-hand fallacy is the opposite of the Gambler's fallacy: thinking an unlikely string of "luck" will continue to hold;
  6. The False Dichotomy or assuming/establishing a binary state when there is none;
  7. Personal Incredulity, not trusting results you don't understand;
  8. Simpson's Paradox illustrating how easy it is to misinterpret data by jumping to conclusions driven by motivated reasoning and not by objectively assessing the evidence;  
  9. P-hacking or identifying trends as statistically significant when they are not;
  10. The Cobra Effect, when an action leads to the opposite of the intended consequences.  

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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. 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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. 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