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This post helps not only motivate, but reinforce the theory that everybody else should be constantly testing, growing, learning, attempting, doing...not looking forward to the next tweet about what to complete and exactly how to complete it. Personally I think like most of us have told designers how exactly to do something but haven't any actual clue exactly what that style of work involves (from the when I first began SEO, I went on about header tags and urged clients to fix theirs - it absolutely wasn't until We used Firebug getting the correct CSS to greatly help a client revamp their header structure while maintaining equivalent design that i really understood the entire image -- it had been a fantastic feeling). I am perhaps not saying that every Search Engine Optimization or digital marketer has to create their own python program, but we have to manage to comprehend (and where relevant, apply) the core concepts that include technical SEO.
Caution should be taken when creating claims of causality even though experimentation or time-ordered research reports have been done. The word causal model must be comprehended to suggest "a model that conveys causal presumptions", definitely not a model that creates validated causal conclusions. Gathering data at multiple time points and using an experimental or quasi-experimental design can help eliminate specific competing hypotheses but also a randomized experiment cannot exclude all such threats to causal inference. Good fit by a model consistent with one causal hypothesis invariably requires equally good fit by another model consistent with an opposing causal theory. No research design, in spite of how clever, will help distinguish such rival hypotheses, save for interventional experiments.
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