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I think this is also why it’s really helpful to simulate these values instead of relying on one’s ability to construct the correct formula. The R package randomizr has some helpful tools in that regard.

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March 15, 2018 at 9:53 am

Yes, it’s a good idea to do simulation and also work through the formula. The simulation is more trustworthy and more flexible; the formula is useful to help understand what’s going on.

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Also simulation is a good check on whether you mixed up changing n versus sd ;-)

Perhaps more importantly you can simulate more realistic assumptions than fit nicely into simple formulas.

Which is more useful to help understand what’s going on, I think, depends on the individual.

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Nit pick: sqrt(sigma^2/(N/2) + sigma^2/(N/2)) = sqrt(2*sigma^2/N + 2*sigma^2/N) = sqrt(4*sigma^2/N) = 2*sigma/sqrt(N) ?

sqrt(sigma^2/(N/4) + sigma^2/(N/4) + sigma^2/(N/4) + sigma^2/(N/4)) = sqrt(4*sigma^2/N + 4*sigma^2/N + 4*sigma^2/N + 4*sigma^2/N) = sqrt(16*sigma^2/N) = 4*sigma/sqrt(N) ?

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Thanks; algebra fixed. I’d remembered the factors of 1 and 2 because with binary outcomes, sigma=0.5, so 2*sigma=1 and 4*sigma=2.

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March 15, 2018 at 10:14 am

I have difficulties to follow the take-home message of this post.

(1) Is it reasonable to expect that interactions are always ~half the size of main effects? Because this seems to be the critical point.

(2) I have even more difficulties when I start thinking about ANOVA as a linear model of the data. I could imagine main effects as predictors, and interactions as their multiplications. At this point, I have the impression that it would be safe to think of these predictors as orthogonal, and hence wondering why should the estimation for these predictors should be more difficult for interaction predictors in comparison to main effect predictors?

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1. I think that considering interactions as half the size of main effects is a useful starting point. The analysis is there, so you can alter it as needed.

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Rainbow tables aren't as colourful as their name may imply but, for a hacker, your password could well be at the end of it. In the most straightforward way possible, you can boil a rainbow table down into a list of pre-computed hashes – the numerical value used when encrypting a password. This table contains hashes of all possible password combinations for any given hashing algorithm. Rainbow tables are attractive as it reduces the time needed to crack a password hash to simply just looking something up in a list.

However, rainbow tables are huge, unwieldy things. They require serious computing power to run and a table becomes useless if the hash it's trying to find has been "salted" by the addition of random characters to its password ahead of hashing the algorithm.

There is talk of salted rainbow tables existing, but these would be so large as to be difficult to use in practice. They would likely only work with a predefined "random character" set and password strings below 12 characters as the size of the table would be prohibitive to even state-level hackers otherwise.

There's an easy way to hack: ask the user for his or her password. A phishing email leads the unsuspecting reader to a faked log in page associated with whatever service it is the hacker wants to access, requesting the user to put right some terrible problem with their security. That page then skims their password and the hacker can go use it for their own purpose.

Why bother going to the trouble of cracking the password when the user will happily give it you anyway?

Social engineering takes the whole "ask the user" concept outside of the inbox that phishing tends to stick with and into the real world.

A favourite of the social engineer is to call an office posing as an IT security tech guy and simply ask for the network access password. You’d be amazed at how often this works. Some even have the necessary gonads to don a suit and name badge before walking into a business to ask the receptionist the same question face to face.

A keylogger, or screen scraper, can be installed by malware which records everything you type or takes screenshots during a login process, and then forwards a copy of this file to hacker central.

Some malware will look for the existence of a web browser client password file and copy this which, unless properly encrypted, will contain easily accessible saved passwords from the user's browsing history.

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It’s easy to imagine that passwords are safe when the systems they protect lock out users after three or four wrong guesses, blocking automated guessing applications. Well, that would be true if it were not for the fact that most password hacking takes place offline, using a set of hashes in a password file that has been ‘obtained’ from a compromised system.

SVA MFA Interaction Design

The MFA in Interaction Design program trains students to research, analyze, prototype, and design concepts in their business, social, and culturalcontexts.

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The two-year graduate program explores the strategic role of interaction design in shaping everyday life, and intends to increase the relevancy of design to business and society so designers can make a difference. It seeks to cultivate interaction design as a discipline and further its visibility as a community of practice. Where we stand in the history of the future is a tremendous opportunity for the design of interactions. We invite all to participate as students, as teachers, as collaborators, and as citizens.

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Millennials trust strangers over friends and family. They lean on UGC for purchases.

They are 3x as likely to follow a brand over a family member in social networks

66% will look up a store if they see a friend check-in

73% have earned and used virtual currency

Gen-Y believes that other consumers care more about their opinions than companies do – that’s why they share their opinions online.

Gen-Y’ers are more connected on Facebook than average users managing a social graph of 696 Facebook friends versus 140.

If knowledge is the key to enlightenment, then perception and imagination are windows to engagement and relevance. We can learn all we want about Millennials, but if we can’t translate that into meaning or substance, we will continue to miss opportunities to build lasting relationships.

The gap isn’t just widening because of the growing pervasiveness of Millennials in our economy. As I introduced in The End of Business as Usual , anyone who places increasing emphasis on technology as part of their daily routine, in many ways, their behavior mimics that of Millennials and as a result, they prove elusive or immune to traditional marketing and service. In the book, I refer to this class of consumer as “ the Connected Customer ” and their behavior is noticeably dissimilar to that of their traditional counterparts. The connected customer is the stranger you must get to know as in comparison to the customers of the past, this group is only growing and it’s traversing demographics. As such, the connected customer becomes what we can or should now refer to as Generation C where the “C” represents connectedness.

No longer can we blame it on the youth. We must blame, if anything, the disruption of technology. Nowadays, age ain’t nothing but a number. It is how people embrace technology, from social networks to smartphones to intelligent appliances, that contributes to the digital lifestyle that is now synonymous with Gen-C.

A recent study published by Nielsen brings Generation C into light. In just one image, we can begin to comprehend the disruption of digital revolution on society. Call it the social economy. Call it the mobile or the app economy. Call it the connected economy. Whatever we call it, this incredible transformation that we’re witnessing, is indeed nothing short of a digital revolution .

The Last 10 Years

274 million American have Internet Access, which is more than double that of 2000.

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64% of all mobile phone time is spent on apps.

42% of tablet owners use them daily while watching TV.

For the first time, the numbers of laptops have surpassed desktops within TV homes.

Women Rule Gen-C

In 2009, I discovered that in social media, women rule . As you can see in Nielsen’s report, women too rule Gen-C. Specifically, they rule social media and online video and TV viewership. With smartphones, men and women are tied in adoption. With tablets however, men rule.

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