Friday, April 22, 2016

Screw College

I’m just like that kid who got into all eight Ivy League schools but chose the University of Alabama because it was free (only he’s way smarter). You see, I applied to a bunch of colleges and a small private one told me they’d give me full tuition. I said, “Where do I sign?” before they could take it back.

My first semester there was a disaster. I had no idea how to study, and I spent most of my time partying. The result? I got a 2.69 GPA. I had to keep a 3.4 for my scholarship, so they sent me an official letter saying I was on probation.

I thought I wanted a degree in finance so I was taking business classes. Then I met a guy who sold weed, so one day I went over to his dorm room. I’d never seen so many computers. He was getting a degree in information systems. He showed me Linux for the first time (it was Red Hat Linux 5.2). I had no idea there were operating systems besides Windows.

I got really obsessed with computers. I learned how to take one apart and put it back together. I built them for friends. I spent hours chatting online on something called IRC. I started working for the campus IT department. I became an expert in Linux. And I quit taking business classes, switching my major to information systems.

When I started college, I thought I wanted to do one thing. I wound up doing something completely different. But I needed to explore different fields to figure that out. Now I wonder if I could have explored on my own, without going to college? Let’s see.

WHY TO GO TO COLLEGE

1. You get to party and hook up

No more curfew, and drugs and alcohol are everywhere. I lived in a fraternity house for three years, and probably drank at least a case of beer a week (always “Natty Light”). By my senior year I had tried all kinds of drugs. As for hooking up? There’s no parents so there’s a lot of it.

2. Maybe an education

You pay tuition to learn stuff from professors. If you go to class, do your work, and get decent grades then they give you a degree at the end. And when you get out, if the job market has a demand for your degree, then you get a 9-5 job and get to start paying back your loans.

WHY TO RE-THINK COLLEGE

1. Loans

The average college grad has $35,000 in loans. Some have two or three times that, like if you completely finance it you might have $100,000-$250,000. I found some examples of this:

This is why some people decide their best repayment strategy is to default. Collectively, we have over $1 trillion in loans, and this doesn’t appear to be changing anytime soon.

2. Low-cost alternatives

There are free and inexpensive options to learn anything online. Like Code Academy,creativeLIVE, Khan Academy, Lynda.com, Udemy, and so on. You can even learn skills you probably need that they don’t even teach in college:

Book writing
Communicating ideas
Critical thinking
Entrepreneurship
Event planning
Improvisational comedy
Influence and persuasion
Networking
Personal finance
Reading comprehension
Storytelling

Some colleges are opening up their curriculums so anyone can take their classes for free. Scott Young got the equivalent of a computer science degree from MIT for $2,000. And MIT isn’t cheap. It costs about $60,000 a year, for a four-year total of $240,000.

When employers were asked if they’d hire Scott without the actual degree, many said yes. It’s because companies like Google don’t care if you don’t have a degree. They want you to be able to solve problems, and having a degree doesn’t prove you can do that.

Is there more to college than a degree? Of course, but those things can’t be worth $238,000.

3. Opportunity cost

Instead of spending $240,000 on MIT, you could spend it so many other ways (this is called the opportunity cost). Here are some ideas from $5,000 to $240,000:

Start a business – $5,000

Any 18 year old can be an entrepreneur. Get a Google-backed nanodegree in entrepreneurship, and build a website to sell your service or product. You’ll learn:

Ideation, and how to choose a product people will buy
Product design and monetization
How to course correct when things don’t right (they never go right)
Maybe even how to hire and manage someone

Have an enriching experience – $10,000

In Europe, it’s common to spend a gap year traveling after high school. Pick someplace the most unlike where you’re from, maybe like Thailand or India. You’ll meet other travelers, see what poverty is, be in super uncomfortable situations (this is always a good thing), and learn how to survive.

Become an angel investor – $100,000

Instead of spending $100,000 on a Stanford MBA, Tim Ferriss created a fund for angel investing. You see, an MBA is two things: a professional network, and the curriculum. By investing in startups he’d build a network, and learn:

Start-up financing
Deal structuring
Rapid product design
How to initiate acquisition conversations

Invest in the stock market – $240,000

Let’s pretend your parents were ultra-wealthy, and at 18 you persuaded them to write you a check for the four-year cost of MIT. Because you’re smart, you invested it in a total market index fund, so by the time you’re 40 you’d be a millionaire (inflation-adjusted, too):

How many MIT alumni have $1,000,000 in the bank by 40? I have no idea. But this is the opportunity cost of using money for one thing (college), rather than another.

IF YOU’RE 18, HERE’S WHAT TO DO

My friend’s kid is a high school senior. He’s also a top performer. He just decided that he wasn’t ready for college. I found this fascinating because kids like him don’t say this. But when you learn that 40% of college kids drop out, maybe more seniors should be following his lead.

When you look at the big picture, not going to college right away is negligible. If you make this choice, go explore fields that interest you. Do you like good food? Get a crappy job in the kitchen of a top restaurant. Medicine? Volunteer at a hospital. Investing? Intern at JP Morgan. Politics? Volunteer on a political campaign. This is your life, remember that you get to make it what you want.

Full > http://mreverydaydollar.com/college/

Overcoming Indoctrination

When a child is taught a set of beliefs and values from birth by people on which he or she is dependent for basic survival, the beliefs and values tend to endure. This appears to be the case even when the beliefs are false and the values are morally suspect. Take something like overt racism or sexism as an example. A child who is raised in an environment where racism or sexism are modeled and taught will adopt these beliefs and values at least temporarily. This should not be surprising to us, as we generally agree that hate and bigotry are learned. The young child does not know any better, and he or she has little choice other than to trust the primary caregivers.

Fortunately, the effects of such an upbringing are not necessarily permanent. With age and life experience, the individual can question aspects of his or her upbringing. Parental values can be critically examined, rejected, and replaced with healthier alternatives. And yet, this process is often lengthy, difficult, and dependent on environmental events. That is, such an individual may need prompting of some sort in order to begin such a critical examination in the first place. This is probably one of the reasons that racist beliefs tend to be a bit more difficult to maintain when one has regular contact with members of various racial groups.

Religious Indoctrination

What does this have to do with atheism? Quite a bit. I tend to think that religious belief and values work in much the same way. The child who is raised by Christian parents and taught from an early age to accept Christian dogma is likely to do just that. And why wouldn't they? People they trust and on whom they depend are presenting this stuff as true; of course the child is going to accept it. While we know that the effects of such indoctrination are not permanent (many of us are former Christians), we should not expect people raised this way to discard their religious beliefs until they have had an occasion to examine them critically. I know quite a few adult Christians who have never critically examined their religious beliefs, and I suspect you do too.

How should we feel about such adults? Well, I don't think that despising them makes much sense. After all, they are doing what we should expect them to do. If they have never seriously questioned the beliefs and values into which they were indoctrinated, I'm not sure why we would expect them to be any different from how they are. Could the same be said of the person who was brought up with racist or sexist beliefs and values? Probably.

This brings us to our central question: if we would like to encourage the religious individual (or sexist individual or racist individual) to critically examine and ultimately discard these beliefs and values, how should we do so? I recognize that some atheists see encouraging religious individuals to discard their religious beliefs as an important goal, and others do not. To the degree that we do accept this as a goal, how do we attempt to accomplish it?

Helping Others Overcome Religious Indoctrination

I would suggest that we begin by realizing that changing someone's mind like this (i.e., helping them realize that what they learned through years of indoctrination is false) is not an easy task and that it must be something they want to do. We cannot simply erase someone's religious beliefs (or racist or sexist beliefs) in an instant and against their will. We might be able to shame, threaten, and humiliate an individual into keeping such beliefs private, but it seems unlikely that such tactics would bring about the sort of changed mind we say we want. Instead, we must encourage and facilitate the process of their critical examination of these beliefs. And in order to do this, we probably need to begin from a place of empathy.

What I mean by beginning from a place of empathy is making an effort to understand the person's experience and worldview. Empathy does not entail agreement; it is merely an effort to see the world from the perspective of the other party. For a quick example of why this is so important, I encourage you to consider the possibility that some religious individuals would feel terribly guilty about questioning their religious beliefs because they would interpret such questioning as a rejection of their parents' values. If their religious beliefs are wrong, this suggests that their parents were wrong. This can certainly provoke guilt and interfere with the process of critically examining such beliefs.

When I see atheists verbally attacking Christians on Twitter, I can interpret it in one of two ways. First, I can tell myself that the atheists are trying, however crudely, to push the Christian to examine his or her beliefs and realize that some of what he or she learned from an early age is likely false. In the best case scenario, perhaps the atheist is simply saying, "I'm an atheist and have managed to live my life free of gods. You can too." I think this can be helpful, especially if the atheist can refrain from personal attacks. Alternatively, I can interpret many of the statements I see from atheists as reflecting strong anti-Christian attitudes and a lack of understanding of how belief works. Those who do little more than angry name-calling may be strengthening the religious beliefs of their audience.

Indoctrination is an effective process that typically unfolds over several years of someone's life. To think we can undo it in a flash - especially in the form of personal attacks - is not just wrong but probably counterproductive. If we are truly interested in changing minds, we need to think in terms of how best to encourage the critical examination of one's beliefs and then support those engaging in such a process. A little patience and empathy might go a long way.

Wednesday, April 20, 2016

The One Way Project w/ Video

One Way Guest Blogs

I started the One Way Project to share stories from my journey abroad and it could not have been more fulfilling. Being able to share my stories, not only let people live through my stories, but it also gave me a platform to relive those moments all over again.
Now I would like to provide that platform to you!
I will be accepting guest blogs from those all over the world. You can be traveling now, or it can be a story from the past. Please read the following criteria below before submitting a post.

GUEST BLOG CRITERIA

Guest blog MUST be about a trip abroad
Blog should be between 450 - 1000 words (if longer, please split into two or more parts)
Please provide a profile picture for your post
Please provide the name of the country you are from
Please provide 1-3 photos for your post (if available)
Please provide a link to your own website if available
Keep language and stories appropriate, not too explicit
Posts will be reviewed and you will receive a notification with a post date once your post has been approved

OPEN FORM

Cool Stuff

http://www.coolthings.com/

http://www.coolthings.com/

Innovative Thinking

IDEAS AND INSIGHT

Looking for some fresh ideas or new perspectives that you can use to improve your company’s performance? If so, we can help. Start by taking our quick “IQ” test to gauge your ability to innovate consistently. Then take a journey through the other resources here. And if that doesn’t help, give us a call or send us a email and we’d be glad to point you in the right direction…

BOOKS

We believe that some of the best life lessons were learned when we were young…

In Lessons from the Sandbox, learn to rediscover the gifts you had as a child and use them to improve corporate performance.

Learn more »

GREAT MOMENTS IN INNOVATIONS

Looking for a touch of inspiration from others who looked at the world with different eyes…

If so, here’s a timeline of some of our favorite innovations. Use it as a starting point for your own exploration of a world full of ideas, opportunities and possibilities

Learn more »

TESTING YOUR CORPORATE IQ

How innovative are you? How innovative can you become. Take the test…

We’ve designed a fun and straightforward “test” that provides a snapshot of your company’s ability to nurture the right new ideas. It’s fast, easy to take, and doesn’t require any studying in advance!

Learn more »

IDEAS TO SPARK YOUR GENIUS

Looking to spark innovation and growth in your organization? We have a few resources to start you off…

We’ve outlined some of our favorite quick start activities and places around the web that offer plenty of insight nuggets. We’ll bet you can’t eat just one.

Learn more »

For the enlightened individuals, simply awesome site

http://www.socialresearchmethods.net/kb/index.php

Regression to the Mean

A regression threat, also known as a "regression artifact" or "regression to the mean" is a statistical phenomenon that occurs whenever you have a nonrandom sample from a population and two measures that are imperfectly correlated. The figure shows the regression to the mean phenomenon. The top part of the figure shows the pretest distribution for a population. Pretest scores are "normally" distributed, the frequency distribution looks like a "bell-shaped" curve. Assume that the sample for your study was selected exclusively from the low pretest scorers. You can see on the top part of the figure where their pretest mean is -- clearly, it is considerably below the population average. What would we predict the posttest to look like? First, let's assume that your program or treatment doesn't work at all (the "null" case). Our naive assumption would be that our sample would score just as badly on the posttest as they did on the pretest. But they don't! The bottom of the figure shows where the sample's posttest mean would have been without regression and where it actually is. In actuality, the sample's posttest mean wound up closer to the posttest population mean than their pretest mean was to the pretest population mean. In other words, the sample's mean appears to regress toward the mean of the population from pretest to posttest.

Why Does It Happen?

Let's start with a simple explanation and work from there. To see why regression to the mean happens, consider a concrete case. In your study you select the lowest 10% of the population based on their pretest score. What are the chances that on the posttest that exact group will once again constitute the lowest ten percent? Not likely. Most of them will probably be in the lowest ten percent on the posttest, but if even just a few are not, then their group's mean will have to be closer to the population's posttest than it was to the pretest. The same thing is true on the other end. If you select as your sample the highest ten percent pretest scorers, they aren't likely to be the highest ten percent on the posttest (even though most of them may be in the top ten percent). If even just a few score below the top ten percent on the posttest their group's posttest mean will have to be closer to the population posttest mean than to their pretest mean.

Here are a few things you need to know about the regression to the mean phenomenon:

It is a statistical phenomenon.

Regression toward the mean occurs for two reasons. First, it results because you asymmetrically sampled from the population. If you randomly sample from the population, you would observe (subject to random error) that the population and your sample have the same pretest average. Because the sample is already at the population mean on the pretest, it is impossible for them to regress towards the mean of the population any more!

It is a group phenomenon.

You cannot tell which way an individual's score will move based on the regression to the mean phenomenon. Even though the group's average will move toward the population's, some individuals in the group are likely to move in the other direction.

It happens between any two variables.

Here's a common research mistake. You run a program and don't find any overall group effect. So, you decide to look at those who did best on the posttest (your "success" stories!?) and see how much they gained over the pretest. You are selecting a group that is extremely high on the posttest. They won't likely all be the best on the pretest as well (although many of them will be). So, their pretest mean has to be closer to the population mean than their posttest one. You describe this nice "gain" and are almost ready to write up your results when someone suggests you look at your "failure" cases, the people who score worst on your posttest. When you check on how they were doing on the pretest you find that they weren't the worst scorers there. If they had been the worst scorers both times, you would have simply said that your program didn't have any effect on them. But now it looks worse than that -- it looks like your program actually made them worse relative to the population! What will you do? How will you ever get your grant renewed? Or your paper published? Or, heaven help you, how will you ever get tenured?

What you have to realize, is that the pattern of results I just described will happen anytime you measure two measures! It will happen forwards in time (i.e., from pretest to posttest). It will happen backwards in time (i.e., from posttest to pretest)! It will happen across measures collected at the same time (e.g., height and weight)! It will happen even if you don't give your program or treatment.

It is a relative phenomenon.

It has nothing to do with overall maturational trends. Notice in the figure above that I didn't bother labeling the x-axis in either the pretest or posttest distribution. It could be that everyone in the population gains 20 points (on average) between the pretest and the posttest. But regression to the mean would still be operating, even in that case. That is, the low scorers would, on average, be gaining more than the population gain of 20 points (and thus their mean would be closer to the population's).

You can have regression up or down.

If your sample consists of below-population-mean scorers, the regression to the mean will make it appear that they move up on the other measure. But if your sample consists of high scorers, their mean will appear to move down relative to the population. (Note that even if their mean increases, they could be losing ground to the population. So, if a high-pretest-scoring sample gains five points on the posttest while the overall sample gains 15, we would suspect regression to the mean as an alternative explanation [to our program] for that relatively low change).

The more extreme the sample group, the greater the regression to the mean.

If your sample differs from the population by only a little bit on the first measure, there won't be much regression to the mean because there isn't much room for them to regress -- they're already near the population mean. So, if you have a sample, even a nonrandom one, that is a pretty good subsample of the population, regression to the mean will be inconsequential (although it will be present). But if your sample is very extreme relative to the population (e.g., the lowest or highest x%), their mean is further from the population's and has more room to regress.

The less correlated the two variables, the greater the regression to the mean.

The other major factor that affects the amount of regression to the mean is the correlation between the two variables. If the two variables are perfectly correlated -- the highest scorer on one is the highest on the other, next highest on one is next highest on the other, and so on -- there will no be regression to the mean. But this is unlikely to ever occur in practice. We know from measurement theory that there is no such thing as "perfect" measurement -- all measurement is assumed (under the true score model) to have some random error in measurement. It is only when the measure has no random error -- is perfectly reliable -- that we can expect it will be able to correlate perfectly. Since that just doesn't happen in the real world, we have to assume that measures have some degree of unreliability, and that relationships between measures will not be perfect, and that there will appear to be regression to the mean between these two measures, given asymmetrically sampled subgroups.

The Formula for the Percent of Regression to the Mean

You can estimate exactly the percent of regression to the mean in any given situation. The formula is:

P_rm = 100(1 - r)

where:

P_rm = the percent of regression to the mean
r = the correlation between the two measures

Consider the following four cases:

if r = 1, there is no (i.e., 0%) regression to the mean
if r = .5, there is 50% regression to the mean
if r = .2, there is 80% regression to the mean
if r = 0, there is 100% regression to the mean

In the first case, the two variables are perfectly correlated and there is no regression to the mean. With a correlation of .5, the sampled group moves fifty percent of the distance from the no-regression point to the mean of the population. If the correlation is a small .20, the sample will regress 80% of the distance. And, if there is no correlation between the measures, the sample will "regress" all the way back to the population mean! It's worth thinking about what this last case means. With zero correlation, knowing a score on one measure gives you absolutely no information about the likely score for that person on the other measure. In that case, your best guess for how any person would perform on the second measure will be the mean of that second measure.

Estimating and Correcting Regression to the Mean

Given our percentage formula, for any given situation we can estimate the regression to the mean. All we need to know is the mean of the sample on the first measure the population mean on both measures, and the correlation between measures. Consider a simple example. Here, we'll assume that the pretest population mean is 50 and that we select a low-pretest scoring sample that has a mean of 30. To begin with, let's assume that we do not give any program or treatment (i.e., the null case) and that the population is not changing over time on the characteristic being measured (i.e., steady-state). Given this, we would predict that the population mean would be 50 and that the sample would get a posttest score of 30 if there was no regression to the mean. Now, assume that the correlation is .50 between the pretest and posttest for the population. Given our formula, we would expect that the sampled group would regress 50% of the distance from the no-regression point to the population mean, or 50% of the way from 30 to 50. In this case, we would observe a score of 40 for the sampled group, which would constitute a 10-point pseudo-effect or regression artifact.

Now, let's relax some of the initial assumptions. For instance, let's assume that between the pretest and posttest the population gained 15 points on average (and that this gain was uniform across the entire distribution, that is, the variance of the population stays the same across the two measurement occasions). In this case, a sample that had a pretest mean of 30 would be expected to get a posttest mean of 45 (i.e., 30+15) if there is no regression to the mean (i.e., r=1). But here, the correlation between pretest and posttest is .5 so we expect to see regression to the mean that covers 50% of the distance from the mean of 45 to the population posttest mean of 65. That is, we would observe a posttest average of 55 for our sample, again a pseudo-effect of 10 points.

Regression to the mean is one of the trickiest threats to validity. It is subtle in its effects, and even excellent researchers sometimes fail to catch a potential regression artifact. You might want to learn more about the regression to the mean phenomenon. One good way to do that would be to simulate the phenomenon. If you're not familiar with simulation, you can get a good introduction in the