Plus, how to avoid the noise in A/B testing.
Mar 26, 2019
Good morning, marketers, are you excited about Oprah + Apple?
What do [Appleâs big announcements yesterday]( about original content development â hello, Oprah! â and new Apple News Plus, Apple TV Plus and Apple Arcade services mean for advertisers?
More of the same (a la, [ITP](), which is to say, not much. Privacy is the name of the Apple game. Content recommendations from Apple News, for example, happen on-device: âAnd that means we donât know what you read. And in addition to that, we donât allow advertisers to track. So what you read about on Apple news will not follow you across the web,â Apple CEO Tim Cook explained Monday.
The companyâs focus on building out revenue from services meant there was little in the way of product announcements. The big differentiation between Apple and competitors -- Google, Amazon, etc. -- is Appleâs privacy POV, and all of the services it announced Monday will be ad-free.
That said, lots to think about in terms of content and influencer marketing â¦
Keep reading more below, including a Pro Tip on avoiding âp-hackingâ in your data analysis.
Ginny Marvin
Editor-in-Chief
Pro Tip
Hereâs how to avoid âp-hackingâ
The more data dimensions you have, the more likely you are to fall into the anti-pattern known as âp-hacking.â P-hacking is the label given to some ways that data analysis can produce seemingly âstatistically significantâ results from pure noise, explains Nathan Labenz of Waymark.
âFor example, consider a simple Facebook A/B test. What often happens when the high-level results prove to be unremarkable is that we dig deeper into the data in search of more interesting findings. Perhaps if we only look at women, weâll find a difference? Segmenting data is generally considered a good practice. But the more you slice and dice the data, the more likely you are to identify spurious results, and the more extreme your p-values must be to have practical weight. This is especially true if your data analysis is exploratory (âletâs check men vs. womenâ) rather than hypothesis-driven (âour research shows that women tend to value this aspect of our product more than men â perhaps the results will reflect that?â).
Nathan offers this advice: âThrow the p=0.05 threshold for âstatistical significanceâ out entirely â a lot of p-hacking stems from people searching endlessly for a result that hits some threshold, and in any case, our decision-making should not rely on arbitrary binaries. And make sure that your data analysis is motivated by hypothesis grounded in real-world considerations.â
[READ MORE »](
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What we're reading
We've curated our picks from across the web so you can retire your feed reader
[With Its New New Brand Identity, ADP Wants to Get People Thinking About Why They Work]( â Adweek
[Why Anheuser-Busch Is Going Big On Ecommerce, With Or Without The Beer]( â Ad Exchanger
[Pitch deck: How Googleâs Waze sells ads]( â Digiday
[5 Skills All Successful Content Writers Have Mastered]( â The Blog Herald
[Freelancer Onboarding Checklist]( â Contently
[Building a Fast Blogging Framework With Chris Brogan at SMMW19]( â Top Rank Marketing Blog
[33 of the best 404 pages]( â Creative Bloq
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