Geckoboard

𝗠𝗰𝗡𝗮𝗺𝗮𝗿𝗮 𝗙𝗮𝗹𝗹𝗮𝗰𝘆 𝘙𝘦𝘭𝘺𝘪𝘯𝘨 𝘴𝘰𝘭𝘦𝘭𝘺 𝘰𝘯 𝘮𝘦𝘵𝘳𝘪𝘤𝘴 𝘪𝘯 𝘤𝘰𝘮𝘱𝘭𝘦𝘹 𝘴𝘪𝘵𝘶𝘢𝘵𝘪𝘰𝘯𝘴 𝘤𝘢𝘯 𝘤𝘢𝘶𝘴𝘦 𝘺𝘰𝘶 𝘵𝘰 𝘭𝘰𝘴𝘦 𝘴𝘪𝘨𝘩𝘵 𝘰𝘧 𝘵𝘩𝘦 𝘣𝘪𝘨𝘨𝘦𝘳 𝘱𝘪𝘤𝘵𝘶𝘳𝘦. Named after Robert McNamara, the U.S. Secretary of Defense (1961-1968), who believed truth could only be found in data and statistical rigor. The fallacy refers to his approach of taking enemy body count as the measure of success in the Vietnam War. Obsessing over it meant that other relevant insights like the shifting mood of the U.S. public and the feelings of the Vietnamese people were largely ignored. When analyzing complex phenomena, we’re often forced to use a metric as proxy for success. However, dogmatically optimizing for this number and ignoring all other information is risky.

𝗦𝗶𝗺𝗽𝘀𝗼𝗻'𝘀 𝗣𝗮𝗿𝗮𝗱𝗼𝘅 𝘈 𝘱𝘩𝘦𝘯𝘰𝘮𝘦𝘯𝘰𝘯 𝘪𝘯 𝘸𝘩𝘪𝘤𝘩 𝘢 𝘵𝘳𝘦𝘯𝘥 𝘢𝘱𝘱𝘦𝘢𝘳𝘴 𝘪𝘯 𝘥𝘪𝘧𝘧𝘦𝘳𝘦𝘯𝘵 𝘨𝘳𝘰𝘶𝘱𝘴 𝘰𝘧 𝘥𝘢𝘵𝘢 𝘣𝘶𝘵 𝘥𝘪𝘴𝘢𝘱𝘱𝘦𝘢𝘳𝘴 𝘰𝘳 𝘳𝘦𝘷𝘦𝘳𝘴𝘦𝘴 𝘸𝘩𝘦𝘯 𝘵𝘩𝘦 𝘨𝘳𝘰𝘶𝘱𝘴 𝘢𝘳𝘦 𝘤𝘰𝘮𝘣𝘪𝘯𝘦𝘥. In the 1970s, Berkeley University was accused of sexism because female applicants were less likely to be accepted than male ones. However, when trying to identify the source of the problem, they found that for individual subjects the acceptance rates were generally better for women than men. The paradox was caused by a difference in what subjects men and women were applying for. A greater proportion of the female applicants were applying to highly competitive subjects where acceptance rates were much lower for both genders.

𝗛𝗮𝘄𝘁𝗵𝗼𝗿𝗻𝗲 𝗘𝗳𝗳𝗲𝗰𝘁 𝘞𝘩𝘦𝘯 𝘵𝘩𝘦 𝘢𝘤𝘵 𝘰𝘧 𝘮𝘰𝘯𝘪𝘵𝘰𝘳𝘪𝘯𝘨 𝘴𝘰𝘮𝘦𝘰𝘯𝘦 𝘤𝘢𝘯 𝘢𝘧𝘧𝘦𝘤𝘵 𝘵𝘩𝘢𝘵 𝘱𝘦𝘳𝘴𝘰𝘯’𝘴 𝘣𝘦𝘩𝘢𝘷𝘪𝘰𝘳. 𝘈𝘭𝘴𝘰 𝘬𝘯𝘰𝘸𝘯 𝘢𝘴 𝘵𝘩𝘦 𝘖𝘣𝘴𝘦𝘳𝘷𝘦𝘳 𝘌𝘧𝘧𝘦𝘤𝘵. In the 1920s at Hawthorne Works, an Illinois factory, a social sciences experiment hypothesised that workers would become more productive following various changes to their environment such as working hours, lighting levels and break times. However, it turned out that what actually motivated the workers’ productivity was someone taking an interest in them. When using human research subjects, it’s important to analyze the resulting data with consideration for the Hawthorne Effect.

𝗥𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻 𝗧𝗼𝘄𝗮𝗿𝗱 𝘁𝗵𝗲 𝗠𝗲𝗮𝗻 𝘞𝘩𝘦𝘯 𝘴𝘰𝘮𝘦𝘵𝘩𝘪𝘯𝘨 𝘩𝘢𝘱𝘱𝘦𝘯𝘴 𝘵𝘩𝘢𝘵’𝘴 𝘶𝘯𝘶𝘴𝘶𝘢𝘭𝘭𝘺 𝘨𝘰𝘰𝘥 𝘰𝘳 𝘣𝘢𝘥, 𝘰𝘷𝘦𝘳 𝘵𝘪𝘮𝘦 𝘪𝘵 𝘸𝘪𝘭𝘭 𝘳𝘦𝘷𝘦𝘳𝘵 𝘣𝘢𝘤𝘬 𝘵𝘰𝘸𝘢𝘳𝘥𝘴 𝘵𝘩𝘦 𝘢𝘷𝘦𝘳𝘢𝘨𝘦. Anywhere that random chance plays a part in the outcome, you’re likely to see regression toward the mean. For example, success in business is often a combination of both skill and luck. This means that the best performing companies today are likely to be much closer to average in 10 years time, not through incompetence but because today they’re likely benefitting from a string of good luck – like rolling a double-six repeatedly.

𝗚𝗮𝗺𝗯𝗹𝗲𝗿'𝘀 𝗙𝗮𝗹𝗹𝗮𝗰𝘆 𝘛𝘩𝘦 𝘮𝘪𝘴𝘵𝘢𝘬𝘦𝘯 𝘣𝘦𝘭𝘪𝘦𝘧 𝘵𝘩𝘢𝘵 𝘣𝘦𝘤𝘢𝘶𝘴𝘦 𝘴𝘰𝘮𝘦𝘵𝘩𝘪𝘯𝘨 𝘩𝘢𝘴 𝘩𝘢𝘱𝘱𝘦𝘯𝘦𝘥 𝘮𝘰𝘳𝘦 𝘧𝘳𝘦𝘲𝘶𝘦𝘯𝘵𝘭𝘺 𝘵𝘩𝘢𝘯 𝘶𝘴𝘶𝘢𝘭, 𝘪𝘵’𝘴 𝘯𝘰𝘸 𝘭𝘦𝘴𝘴 𝘭𝘪𝘬𝘦𝘭𝘺 𝘵𝘰 𝘩𝘢𝘱𝘱𝘦𝘯 𝘪𝘯 𝘧𝘶𝘵𝘶𝘳𝘦 𝘢𝘯𝘥 𝘷𝘪𝘤𝘦 𝘷𝘦𝘳𝘴𝘢. This is also known as the Monte Carlo Fallacy because of an infamous example that occurred at a roulette table there in 1913. The ball fell in black 26 times in a row and gamblers lost millions betting against black, assuming the streak had to end. However, the chance of black is always the same as red regardless of what’s happened in the past, because the underlying probability is unchanged. A roulette table has no memory. When tempted by this fallacy, remind yourself that there’s no rectifying force in the universe acting to ‘balance things out’!

𝗦𝗮𝗺𝗽𝗹𝗶𝗻𝗴 𝗕𝗶𝗮𝘀 𝘋𝘳𝘢𝘸𝘪𝘯𝘨 𝘤𝘰𝘯𝘤𝘭𝘶𝘴𝘪𝘰𝘯𝘴 𝘧𝘳𝘰𝘮 𝘢 𝘴𝘦𝘵 𝘰𝘧 𝘥𝘢𝘵𝘢 𝘵𝘩𝘢𝘵 𝘪𝘴𝘯’𝘵 𝘳𝘦𝘱𝘳𝘦𝘴𝘦𝘯𝘵𝘢𝘵𝘪𝘷𝘦 𝘰𝘧 𝘵𝘩𝘦 𝘱𝘰𝘱𝘶𝘭𝘢𝘵𝘪𝘰𝘯 𝘺𝘰𝘶’𝘳𝘦 𝘵𝘳𝘺𝘪𝘯𝘨 𝘵𝘰 𝘶𝘯𝘥𝘦𝘳𝘴𝘵𝘢𝘯𝘥. A classic problem in election polling where people taking part in a poll aren’t representative of the total population, either due to self-selection or bias from the analysts. One famous example occurred in 1948 when The Chicago Tribune mistakenly predicted, based on a phone survey, that Thomas E. Dewey would become the next US president. They hadn’t considered that only a certain demographic could afford telephones, excluding entire segments of the population from their survey. Make sure to consider whether your research participants are truly representative and not subject to some sampling bias.

𝗚𝗲𝗿𝗿𝘆𝗺𝗮𝗻𝗱𝗲𝗿𝗶𝗻𝗴 𝘛𝘩𝘦 𝘱𝘳𝘢𝘤𝘵𝘪𝘤𝘦 𝘰𝘧 𝘥𝘦𝘭𝘪𝘣𝘦𝘳𝘢𝘵𝘦𝘭𝘺 𝘮𝘢𝘯𝘪𝘱𝘶𝘭𝘢𝘵𝘪𝘯𝘨 𝘣𝘰𝘶𝘯𝘥𝘢𝘳𝘪𝘦𝘴 𝘰𝘧 𝘱𝘰𝘭𝘪𝘵𝘪𝘤𝘢𝘭 𝘥𝘪𝘴𝘵𝘳𝘪𝘤𝘵𝘴 𝘪𝘯 𝘰𝘳𝘥𝘦𝘳 𝘵𝘰 𝘴𝘸𝘢𝘺 𝘵𝘩𝘦 𝘳𝘦𝘴𝘶𝘭𝘵 𝘰𝘧 𝘢𝘯 𝘦𝘭𝘦𝘤𝘵𝘪𝘰𝘯. In many political systems, it’s possible to manipulate the likelihood of one party being elected over another by redefining the political districts – include more rural areas in a district to disadvantage the party that’s more popular in cities etc. A similar phenomenon known as the Modifiable Areal Unit Problem (MAUP) can occur when analyzing data. How you define the areas to aggregate your data – e.g. what you define as ‘Northern counties’ – can change the result. The scale used to group data can also have a big impact. Results can vary wildly whether using postcodes, counties or states.

GROW Europe was our first HubSpot event - won’t be the last! Fun to chat with so many HubSpot (and Geckoboard!) customers, agencies, practitioners, vendors and HubSpotters (is that the right nomenclature?!) in the same space. I think everyone would agree Karren Brady ‘s keynote was a massive shot of inspiration too 🙌

Had a blast at HubSpot GROW yesterday with Nick Smith and Dale Pearson. HubSpot's user centric approach really shone through - also the pace of improvement. It was also a delight to finally meet Russell Bradley-Cook ⚡️and Daphne Costa Lopes in person, catch up with other tech partners Christie Fast, Tom Gray, and meet some customers in the flesh! Looking forward to the next one.

𝗙𝗮𝗹𝘀𝗲 𝗖𝗮𝘂𝘀𝗮𝗹𝗶𝘁𝘆 𝘛𝘰 𝘧𝘢𝘭𝘴𝘦𝘭𝘺 𝘢𝘴𝘴𝘶𝘮𝘦 𝘸𝘩𝘦𝘯 𝘵𝘸𝘰 𝘦𝘷𝘦𝘯𝘵𝘴 𝘰𝘤𝘤𝘶𝘳 𝘵𝘰𝘨𝘦𝘵𝘩𝘦𝘳 𝘵𝘩𝘢𝘵 𝘰𝘯𝘦 𝘮𝘶𝘴𝘵 𝘩𝘢𝘷𝘦 𝘤𝘢𝘶𝘴𝘦𝘥 𝘵𝘩𝘦 𝘰𝘵𝘩𝘦𝘳. Global temperatures have steadily risen over the past 150 years and the number of pirates has declined at a comparable rate. No one would reasonably claim that the reduction in pirates caused global warming or that more pirates would reverse it. But it’s not usually this clear-cut. Often correlations between two things tempt us to believe that one caused the other. However, it’s often a coincidence or there’s a third factor causing both effects that you’re seeing. In our pirates and global warming example, the cause of both is industrialization. Never assume causation because of correlation alone – always gather more evidence.