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Virtually all data one might want to collect as part of doing personal science, has some form of timestamp associated with it; that is, the moment when the data was collected. Unfortunately handling dates and times isn't as straightforward as one might assume. The challenge of correctly doing so is often only half-jokingly called "one of computer science's hardest problems". Why are dates and times difficult to catalog? There are [https://www.zainrizvi.io/blog/falsehoods-programmers-believe-about-time-zones/ many reasons], including traveling across time zones and daylight savings time.

Virtually all data one might want to collect as part of doing personal science, has some form of timestamp associated with it; that is, the moment when the data was collected. Unfortunately handling dates and times isn't as straightforward as one might assume. The challenge of correctly doing so is often only half-jokingly called "one of computer science's hardest problems". Why are dates and times difficult to catalog? There are [https://www.zainrizvi.io/blog/falsehoods-programmers-believe-about-time-zones/ many reasons], including traveling across time zones and daylight savings time.

It can be important to understand this distinction both when comparing different sleep metrics to each other, in order to compare data from the correct dates but also when trying to understand how sleep might affect other variables. Not understanding for which day the sleep is recorded can easily result in being off-by-one.

It can be important to understand this distinction both when comparing different sleep metrics to each other, in order to compare data from the correct dates but also when trying to understand how sleep might affect other variables. Not understanding for which day the sleep is recorded can easily result in being off-by-one.

 

[[Category:Topics]]

[[Category:Data analysis]]