250221_Do We Always Need a Team - ENG USA

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When & Where Did It Happen? • Be as specific as possible. What Was the Impact—Both Actual and Potential? • Document what has already happened. • Consider how bad this could have been had it gone undetected. • Assess whether this risk has existed for some time without being addressed. By taking the time to clearly define the problem upfront, a solo investigator sets the stage for meaningful analysis and real improvement. Step 3: Analyzing Causes—Going Beyond the Immediate to the Contextual Once you’ve gathered evidence and framed the problem, it’s time to analyze causes. This is where working alone presents a unique challenge—without a team, it’s easier to settle on surface-level explanations or unconsciously confirm your own assumptions. To counter this, you have to be deliberate about challenging your own thinking and pushing beyond obvious causes. Think Beyond a Simple Cause-and-Effect Chain One of the biggest mistakes in solo RCA work is treating causation as a single-threaded, linear path—but most real-world failures don’t happen that way. Instead of asking, “What is the cause?” ask, “What combination of causes led to this outcome?” Many failures occur due to multiple interacting causes that together created the conditions for the event. This is where conditional logic becomes critical. Ask two questions: • What caused this effect? • Every time this cause occurs, does it always result in this effect? Instead of stopping at a single cause , look for combinations of contributors that made the failure possible. And don’t stop at human error – more on that here! Bias Management in Cause Analysis When working solo, bias management is especially important because you don’t have a team to challenge your thinking. Without that external feedback loop, it’s easy to fall into common cognitive traps that distort cause analysis. Some key biases to watch for: • Confirmation Bias – The tendency to favor evidence that supports what you already believe while ignoring or downplaying contradictory data. A good practice is to actively look for disconfirming evidence —ask yourself, What facts would prove me wrong?

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