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Evidence in Flight 370: Snowflakes Add Up

Strength in Numbers

How much does a snowflake weigh? How about a drop of water? If you’ve ever shoveled snow, or picked up a large jug of water, you know that the weight adds up. The same principle holds true with evaluating evidence. When starting an analysis, we often find little hints that slightly support one hypothesis over the other. Many people might think that these clues wouldn’t really have an impact. Or they might assume that a piece of evidence with a significant influence on the hypotheses renders other evidence meaningless.

However, just like snow, those little pieces of evidence can add up. It’s hard to shovel up thousands of snowflakes at a time (even though individually they’re almost weightless). Likewise, enough evidence pointing in the same direction can have a weighty influence (even if each individual “proof” is not that strong independently).

 

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Getting to the Essence

On Monday, John went to the mall. John left Green’s Grocery at 7:47 AM and drove home in his 2007 Honda Pilot. John forgot his blue umbrella in the store. That night John’s neighbor Martha, a retired engineer who lives down the block from him, called him. Martha had found the umbrella and was coming to return it. Is that a miracle? Or a largely predictable outcome? The answer depends on which of the many coincidences we consider.

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Calculating Accurate Inputs

Serial: A Game of Numbers

Every analyst has their methods: take a poll, measure social media buzz, weigh various key factors. Rootclaim’s analysis of Serial: Who killed Hae Min Lee provides a good example of how the Rootclaim system uses hard numbers in order to “calculate reality” – to determine mathematically which hypothesis is the most likely.

Rootclaim recently took on one of the most controversial criminal convictions: that of Adnan Syed, sentenced to jail for murdering his ex-girlfriend Hae Min Lee. Syed’s conviction has been featured in the podcasts Serial and Undisclosed, and followers of the case have debated the minutiae on forums such as Reddit. Until now, discussion forums have focused on how a few particular pieces of evidence proved one hypothesis or another. Rootclaim has put together the first concerted effort to gather all the relevant information into one cohesive analysis.

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Innoculate Your Arguments with Data and Reason

The MMR Vaccine Controversy

The controversy over whether and how often to vaccinate children remains a volatile one. But how much does this debate match the empirical data and objective reasoning? The Rootclaim analysis of the MMR vaccine and its alleged ties to autism illustrates the challenges of objective, rational decision-making. Continue reading

Unraveling the Mystery of Trump’s Hair

What’s the Deal with Trump’s Hair?

Given Donald Trump’s flair for controversy, it’s not surprising that even his hair would be the subject of debate. In “What is the story behind Donald Trump’s hair?”, Rootclaim analyzes the most popular claims about the Donald’s trademark hair-covering to cut through the uncertainty.

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Rootclaim: Calculating Reality

Today we’re officially announcing the launch of Rootclaim – a collaborative analysis platform that outperforms human reasoning in understanding complex issues.

Calculating All Sides of the Story

The platform integrates all available evidence, assesses it for credibility and then uses probabilistic models to reach conclusions about the likelihood of competing hypotheses.
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False Dilemma Fallacies–Finding the Gray

The False Dilemma

The Fallacy of Presumption can take many forms. One common example is the false dilemma. This fallacy is also known as the false binary or false dichotomy. As its name indicates, the false dilemma divides a scenario into only two alternatives. When the situation calls for a yes/no answer, that works fine. But in many situations, things are more complex. Thus the false dilemma tricks you into choosing between two imprecise, inaccurate or otherwise flawed options.

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The Straw Man and Other Fallacies of Relevance

What do ad hominems, appeals to authority, red herrings and the straw man argument have in common? Each of these is an example of the Fallacy of Relevance.

Fallacies of Relevance are logical fallacies in which a key part of the argument is actually irrelevant to its conclusion. People often find these fallacies hard to detect.  Understanding how to construct and take apart an argument can help you avoid falling for such fallacies.

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Avoid Circular Reasoning with Rootclaim

Circular reasoning is a common logical fallacy. It is a form of the Fallacy of Presumption. In these fallacies, an argument sounds coherent and compelling, but is actually dependent on one or more faulty or unproven arguments . In circular reasoning, each claim loops around and rests on the assumption of one of the other claims. Thus, no single starting point is ever conclusively and independently established.

“Donald Trump says that he is trustworthy. But Donald Trump is a liar. You can’t trust liars. When Donald Trump says that he is trustworthy, he is lying.”

“Crooked Hillary is crooked because she’s untrustworthy. She can’t be trustworthy because she’s crooked.”

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When We Estimate the Inputs, How Valid Are the Outputs?

One of the arguments against the validity of the Bayesian approach is that, in some cases, it’s hard to pin a precise number on a probability. When we can find good and relevant statistics to back up the assessment of a likelihood, this isn’t such an issue. But in the real world, some of the time we don’t have good numbers. Or we have the numbers on something similar to what we’re looking for – but not quite the same thing.

So what happens in those cases when we have to estimate the inputs?

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