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Notes from an "alternate universe". Introduction to a new way of thinking.
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(The following is provided for illustration only...)
We know very little compared with our own subconscious.
When we "externalize the desire or want" we are communicating with our subconscious.
We can ask questions about Point B but it is not the questions nor the answers that get us there.
Our subconscious asks no questions nor provides any answers.
The questions are like queues for our subconscious to interpret. We can then interpret the "result".
The waking mind asks the questions and interprets what may be seen as answers.
When you say, "So, the whatever perspective we each currently experience comes from the whichever answers we have given to our whichever questions we asked in the first place." we can consider that our current perspective is not based on the questions of our waking mind.
For, "dont ask how to win the lottery, ask how you can have an abundance of money instead, for example", [I don't think this is a direct quote, but I will respond as though it was] it is not meant to be a question for the subconscious but more of a practical question about how to go about making a more effective model.
I suppose the ultimate question is really "How do I get from Point A to Point B?"
The answer is that our waking mind represents what Point B is and our Genius (subconscious) makes it more relative to our current perspective.
I love illustrative examples. Thanks.
So, we are teaching our waking mind what the subconscious already knows. Since we don't know how we use the Genius, are we actually functioning only through the subconscious currently?
You could also say that we are that which makes up the subconscious.
It's filled with all kinds of everything.
Parts that know, parts that don't know.
We are the story of a part of it, and it is us.
The cells in your hand are asking the same questions :)
Iow, what's the difference in the waking mind that knows it wants to get to Point B, but doesn't know how it happens....as compared to the waking mind that does know how it happens.
Get to point B; waking mind does not know = 4 years, 3 months (exmaple)
Get to point B; waking mind knows how = 1 week
Get to point B; waking mind
knows and understands = a few nanoseconds
In the same way, your body "understands" things that the cells in your hand do not. It is experiencing time and space differently because of this understanding.
The understanding, however, is not a mental process.
It is the relationship of values in its perspective.
Hope this helps.
Quoting Chaol: "Parts that know and parts that don't know."
I see this for sure. The way I'm expanding my perspective is trying a lot of new stuff. I think the most interesting discovery I've made is how easily I consciously make things unconscious...without realizing I"m even doing so!!!!!!
When I took the walk the other day where I focused on things I didn't like, I was shocked at how easily I made sure I didn't notice those things.
Today, I looked for new life in the things I love (per your suggestion) while I walked, and was amazed again at the conscious repression into unconsciousness....and also how to retrieve from the unconsciousness by noticing..... instead of sinking something out of "view."
I listened to a car drive by and kept listening as it went down the street....and could hear it almost a block away!!!!! Just because I chose to continue listening instead of sinking the sound into unconsciousness. Pretty neat!
Thank you for the example above of waking mind and really waking mind getting to point B! Very nice.
The body only knows things I don't know as long as I keep my perspective that way. correct? I'm stumbling over your statement: "It is the relationship of values in its perspective." This makes it sound as if the body has a different perspective to mine.
Yes, the body has a different perspective (though again there is only one perspective that, when used, appears to be infinite).
There's an entire life in just one breath. Can you imagine living and working your entire life towards the purpose that the body that you unknowingly help to make up could take a single breath?
The cells know not what they do from your point of view because from their point of view it is something very different. They dream about you as you dream about your other lives, to be sure ;)
It is translated into something useful and relative for each
We live and work our entire lives toward a purpose that we know not, and see it very differently than how our 'expanded perspective' would see it.
The perspectives are equally valid, again, because each is relative to what surrounds it.
I don't want to give the impression that if you have this 'expanded perspective' that you will miss all of the joys of life... walking along a path or having interesting conversations with friends. It is just that you experience so much more of these walks and conversations and would have a new kind of walk and conversation that would be completely fulfilling in your life and seem to take forever. (But from our now-human perspective this kind of life may be hundreds or thousands years-long.)
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Cairo Electricity Production's CEO, Mohamed Mokhtar, stated that progress is underway on the Egyptian New Capital's power plant, estimated to cost $2b in investments, with 30% of the project completed.
General Electric Co. has won a $15.5b contract to supply turbines to Wudongde hydropower plant being built in China.
General Electric said in a statement on Friday it had delivered 34 gas turbines to Egypt out of a planned 46 turbines total as part of a project to provide 2.6 gigawatts of power to the grid by May 2015.


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Chad Brown
Basket Case 2
This entertaining sequel follows Duane Bradley (Kevin Van Hentenryck) and his mutant twin, Belial, as they take refuge in a Staten Island mansion owned by eccentric Granny Ruth (Annie Ross) and occupied by other horribly deformed freaks. Trailed by the tabloid media, the siblings join forces with the menagerie of weirdos, Granny and her daughter (Heather Rattray) to get rid of the pesky press … permanently. Kathryn Meisle and Ted Sorel also star.


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On this date in 1979, the first Gay Rights March on Washington, D.C., the National March on Washington for Lesbian and Gay Rights, demanded "an end to all social, economic, judicial, and legal oppression of lesbian and gay people." The rally drew 200,000 people.
Current subscribers have full access to all online news content as part of their subscription. If you are a current subscriber, please log in with your website account information for full access. If you are a current subscriber, but have not yet registered for access to the website, please call our Circulation Department at (330) 821-1200


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It's amazing how fast you can lose 3G signal in a building – we've had experiences where we could see a phone mast through a window, yet couldn't even get a single lousy bar on our phone. Kineto Wireless' 'Signal Boost' app aims to cure these ills, by hijacking Wi-Fi networks for calls and texting.
The Kineto Wireless Signal Boost app comes pre-installed on select handsets (see end of article for a full list), and can be used by existing Orange customers here in the UK. Using it is simple as pie. When you're connected to a Wi-Fi network, the Signal Boost icon flashes up on your notification bar and your signal bars will grey out. Just like this, in fact:
When the house is green, it means you're good to go. Any calls, texts and web stuff from this point will be relayed over the Wi-Fi network instead of 3G. Unfortunately, unlike calls made over Skype, these will still eat into your monthly minute and text allowance.
We tested Signal Boost over our work network, and call quality on the whole was strong. We could clearly make out the other person, and were clearly heard too. Quality was still fine when we roamed away from the Wi-Fi router, although there was a lot of crackling at one point near the edge of the network's range.
When we strayed too far the call simply cut out, instead of swapping back onto 3G. We were expecting an audible warning as promised in the release, but over a number of tests the call simply ended. After a pause, the phone then picked up the 3G signal again.
If your home or office is a 3G signal black hole and you still need to make standard phone calls with your number, the Signal Boost app is a great solution. We imagine small businesses would get the most from it. Of course, for friendly chats we personally still prefer Skype - you may have to set up an account, but it's completely free to use, available on all handsets, and you can get some video chat on the go.
Compatible mobile phones: HTC Desire S, HTC Wildfire S, HTC Cha Cha, LG Optimus One, LG ME, Orange Monte Carlo, Orange Barcelona, Orange Stockholm


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It's Poem In Your Pocket Day, and like a springtime bird still dazed by the snow, I dart, twisting my head, in unbelief at all the food. It's Poem In Your Pocket Day, and everywhere is a poem. Twist your gaze, grab some unbelief: the snow is gone. Look. Look at the food. http://www.poets.org/page.php/prmID/406 [Read more…]
National Poem In Your Pocket Day #pocketpoem
April 18, 2013 by 1 Comment


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"It's nice to be able to play people and not worry about stamina and fouls so much," UNC coach Roy Williams said.
Still, a phenomenal late-game rally nearly catapulted the Tar Heels (19-11) into the Sweet 16. But when Rashad McCants' last-second attempt to nail a game tying three – that would later be ruled as a two-point attempt – fell short, Roy Williams' quest for his first national championship ended.
"I thought my foot was on the line, but at the same time I was trying to get away from the defender," said McCants, who scored a game-high 27 points.
Yet were it not for the heart the Tar Heels displayed, McCants would have never gotten the opportunity.
Trailing 73-66 with 1:50 left to play, Sean May found a wide-open McCants, who appeared surprised by the pass and was unable to convert his sixth three-pointer of the game. However Raymond Felton managed to find the mark on the Tar Heels' next possession, and with 1:14 left, they trailed by five and hope remained.
After Brandon Mouton made only one of two free throws, the slumping Melvin Scott hit a huge three to make it 75-72 with 25.9 seconds left.
But there would be no miracles, as Mouton converted on two free throws. Felton hit another three to cut the score to two with 11.1 seconds left, however the Tar Heels could not stop the ensuing inbounds pass. May had to spend his fifth foul, which enabled Raleigh Enloe product P.J. Tucker to hit one of two free throws for the game's final margin.
"We dug ourselves a little too big of a hole against a very good team," Williams said fighting back tears after the game. "We were a couple of little mistakes from having a lot of fun. I congratulate Texas because they made plays. They made free throws and they made shots when they had to make them.
"The scoreboard says 78-75 and that's the bottom line."
McCants, the ACC's leading scorer, went for 27 points and Jawad Williams added 17 to lead four Tar Heels in double-figures. May and Felton each added 11 points.
Now Carolina fans will have to wait and see if UNC's talented trio of underclassmen will all return, for what could be an incredible team next season.
"I expect to have all of them back," Williams said. "I'm hopeful they'll hurt long enough this spring, summer and fall to make the commitment to making their individual games better and be willing to make the team better."
As McCants, Felton and Jawad Williams left the podium, while Roy Williams remained for more questioning, each player tapped their coach on the back in a show of support.
"We came a long way as a team," Felton said. "Tonight was a hard loss. We are all upset, but we have to try and build on the things we were doing."
"I thought it was a very interesting season," McCants added. "I never expected it to end like this. I think coming back next year, we are going to be a lot more determined and focused on the little things."
The first half was indicative of the match-up it was hailed to be, with both teams stretching their legs after being subjected to their previous opponents' ball control tactics. Texas took a 44-39 lead at the half behind 60-percent shooting, including 40-percent from beyond the arc. After trailing by as many as ?, the Tar Heels, which out-rebounded the so-called bigger Longhorns, went on an 8-2 run to cut the deficit to 44-39 before intermission.
Then, despite getting possession and executing two defensive stops to start the second half, Carolina could not convert and quickly went back down by nine. After back-to-back baskets by Williams and McCants, UNC was only on the slight downside of a 48-44 score.
But then the Tar Heels went cold. With near to nothing available inside after May picked up his third personal foul, Texas scored nine unanswered points and opened up a seemingly insurmountable 13-point cushion.
"It helped us a lot [when May got his third]," UT forward Jason Klotz said. "We were up and just had to take care of the ball. We put some bodies out there, and it's important in a crucial game like this when you have to crash the boards."
Finally, McCants nailed his fifth and final three to cut the lead to 57-47 with 12:36 left.
After Williams called timeout, a baseline triple-team forced Klotz to double dribble, which was followed by two Jawad Williams' free throws that made it 57-49. Then came a steal and layup and a post-up bucket by McCants, followed by a May put back of a Felton miss, and all of a sudden Carolina was back within two.
"We found a way to finish the game off," UT coach Rick Barnes said. "It wasn't pretty; we had a hard time handling the ball tonight, but you have to give North Carolina credit. They played great defense.
"We talked about how they will always keep playing," he said. "They are big, long, athletic, and they come with big traps. We had to keep looking to score."
After keeping his decorum in check throughout the weekend, Barnes triumphantly shouted as he was leaving the arena, "You've got to believe!"
Texas (25-7) will face the winner of Sunday's game between Xavier and Mississippi State next Friday in Atlanta.


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I realize I risk being kicked out of the Nations I belong for compiling this list. I also realize I could be blocked in Nations where I'm not a member. Even worse, albeit the chances are virtually zero, I could be banned from Newsvine.
But what the hell. If I can't have fun on Newsvine, why be here anyway. And without any further ado, here are my top Five Nations:
1. SiN. No surprise this Nation sits at number one with me. It's where I feel most comfortable cussing and discussing. Serious debate is often surrounded by satire which is usually both informative and entertaining in this "open-minded" Nation.
2. Progressive State of Mind. The name of this Nation says it all. It has one of the largest memberships on the vine. But that's not why it ranks number two on my list. It's because this Nation's admins have IQs which are among the highest on the vine.
No peddling "snake oil" in this Nation. No one's buying. Besides "snake oil" peddlers seem to avoid this Nation for some reason.
3. ButtHeads. This Nation is number one in my heart. Its because of its female admins. It's hard for me to get down on the female admins here ... because they're the ones who are always willing to get down with it.
Getting down in ButtHeads never fail to get me up. Just the thought of this Nation makes me want to butt my head against something pleasing.
4. NoDeletesorBlocks. I respect the hell out of this Nation to the high heavens. It really means what it says, "We don't censor." The admins here, to their credit, will put up a target and stand in front of it. And allow anyone to shoot at that target.
They will even sometimes supply the ammunition. What could be better than that? Especially when it's so easy to hit the target. This is the reason this nation is number four on my list.
5. Whores and Sluts. My kind of people. In a perfect world, this Nation would be the undisputed number one on my list.
But for some reason, as far as I can see, the debauchery and SiNfullness of this Nation's comments haven't been contagious enough to give enough viners "the claps." But this doesn't stop me from trying to catch what I need from this Nation.
Muffins! This Nation very slightly missed my top five. Perhaps some day it will. Whenever I think of this Nation, licking and eating muffins always enter my thoughts.
Muffins giving me the pleasure of licking and eating them ... me giving muffins the pleasure of licking and eating them. After all, the motto of this Nation's admin is: "Bump, bump, sugar lump rump!" I like it. I like it a lot.
I must disclose I'm not a fan of Nations. But there are some very good things about Nations. To argue otherwise would be foolish.
It's all about being positive and not negative. What do you think? What are your top five or top ten favorite Nations on the vine?
Have no fear. Disagreeing with me will not turn my smile upside down. Your opinion may even be very helpful to me and others. It may also allow us to be better able to view Nations from your perspective ... a perspective which we are currently unable to see.
For the record, I didn't shoehorn the other 13 Nations where I have membership between numbers 2 and 3. That would have completely destroyed my creditability for being an objective and nonbiased SiNful Saint.


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Deadlocked 3 Is a 2012 Zombie Book Written By A.R Wise It Is Also The Third Novel In The Deadlocked Series And The Second Sequel To The First Novel
The Deadlocked series continues as Billy heads into the city in search of his mother. During his quest, he will meet a strident military figure that will alter his journey and two brothers that will become his closest allies. However, his trip will endanger the family he has only just met, and he might not be able to save them. Billy's journey into the city will reveal many of the secrets of this penultimate novella. No characters are safe from the terror that has engulfed the city, and as the motives of those in charge begin to be revealed, the fate of all mankind will be in question.


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Durban, Oct 18: IPL champions Kolkata Knight Riders crashed out of Champions League T20 after the group match against Perth Scorchers was called off due to rain at Durban on Wednesday.
Aware of rain playing spoilsport, Gautam Gambhir decided to bowl first after winning the toss and restricted Perth Scorchers to 91/2 off 14 overs as heavy rain stopped play.
Herschelle Gibbs was dismissed for a nought while Simon Katich and Shaun Marsh added 72 runs for the second wicket. Shaun Marsh (38) and Simon Katich (43*) ensured that Perth Scorchers do not lose too many wickets.
Kolkata Knight Riders' run in the Champions League has been very disappointing, as they lost two matches to Delhi Daredevils and Auckland Aces while the third match was washed out due to rain.
Kolkata playing their last group match against Titans in Cape Town, would like to finish the tournament on a high after being crashed out of Champions League T20.


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Champion jockey Richard Hughes landed a first British Classic when he guided Sky Lantern to victory in the 1,000 Guineas at Newmarket on Sunday for trainer Richard Hannon.
Hughes, 40, won the flat jockey's title for the first time last season despite serving a 50-day ban, controversially imposed by stewards in India, which kept him sidelined until the beginning of May.
Hughes and Hannon, his father-in-law, were out of luck with Toronado, who the jockey had touted as "possibly the best I've ever ridden" in the 2,000 Guineas on Saturday but Sky Lantern made up for the disappointment in the fillies-only race with a half-length success.
"It's about time!" Irishman Hughes told the Racing Post. "I believed in this filly and she came good. She is a very good filly who loves fast ground and she deserves this."


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Decision makers
- Katie Couric Show
Katie Couric Show: Apologize for the disregard of anti-human trafficking efforts.
Katie Couric invited Jada Pinkett Smith on her show to shed light on the anti-human trafficking efforts of her organization and in the same week hosted 50 Cent (self proclaimed P.I.M.P). Glorification of the PIMP culture, regardless of involvement, continues to promote the degradation of the victims of sex trafficking and all slave labor and removes the power of their voice.
This link explains the issue of PIMP glorification and the a part of the current movement to undermine and expose it:
- Katie Couric Show
Ms. Couric,
We were shocked and surprised to learn that within the same week, your show condemned sex trafficking by having Jada Pinkett Smith and survivors of DMST on your show while glorifying the PIMP culture that promotes DMST by welcoming self-proclaimed PIMP 50cent as a guest! There is a direct connection between the commercial sexual exploitation of girls in America (and around the world) to the culture of the 'American Pimp'. Glorification of the PIMP culture, regardless of involvement, continues to promote the degradation of the victims and removes the power of their voice. As advocates for children and as modern day abolitionists we regret our support of your show recently. On behalf of young boys and girls everywhere, we cannot and will not stand by while media and pop-culture contributes to the sale of children as sexual commodities for ratings sake. I respectfully ask that you issue a statement apologizing for the disregard of Ms. Jada Pinkett Smith, on behalf of her organization Don't Sell Bodies, and all of the survivors out there trying to fight for a world where girls are not for sale.
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Things I Have Learned (So Far)
- Naomi Nelson
- 2 years ago
- Views:
1 Things I Have Learned (So Far) Jacob Cohen New York University ABSTRACT: This is an account of what I have learned (so far) about the application of statistics to psychology and the other sociobiomedical sciences. It includes the principles "less is more" (fewer variables, more highly targeted issues, sharp rounding off), "simple is better" (graphic representation, unit weighting for linear composites), and "some things you learn aren't so." I have learned to avoid the many misconceptions that surround Fisherian null hypothesis testing. I have also learned the importance of power analysis and the determination of just how big (rather than how statistically significant) are the effects that we study. Finally, I have learned that there is no royal road to statistical induction, that the informed judgment of the investigator is the crucial element in the interpretation of data, and that things take time. What I have learned (so far) has come from working with students and colleagues, from experience (sometimes bitter) with journal editors and review committees, and from the writings of, among others, Paul Meehl, David Bakan, William Rozeboom, Robyn Dawes, Howard Wainer, Robert Rosenthal, and more recently, Gerd Gigerenzer, Michael Oakes, and Leland Wilkinson. Although they are not always explicitly referenced, many of you will be able to detect their footprints in what follows. Some Things You Learn Aren't So One of the things I learned early on was that some things you learn aren't so. In graduate school, right after World War II, I learned that for doctoral dissertations and most other purposes, when comparing groups, the proper sample size is 30 cases per group. The number 30 seems to have arisen from the understanding that with fewer than 30 cases, you were dealing with "small" samples that required specialized handling with "small-sample statistics" instead of the critical-ratio approach we had been taught. Some of us knew about these exotic small-sample statistics in fact, one of my fellow doctoral candidates undertook a dissertation, the distinguishing feature of which was a sample of only 20 cases per group, so that he could demonstrate his prowess with small-sample statistics. It wasn't until some years later that I discovered (mind you, not invented) power analysis, one of whose fruits was the revelation that for a two-independent-group-mean comparison with n = 30 per group at the sanctified two-tailed.05 level, the probability that a medium-sized effect would be labeled as significant by the most modern methods (a t test) was only.47. Thus, it was approximately a coin flip whether one would get a significant result, even though, in reality, the effect size was meaningful. My n = 20 friend's power was rather worse (.33), but of course he couldn't know that, and he ended up with nonsignificant results with which he proceeded to demolish an important branch of psychoanalytic theory. Less Is More One thing I learned over a long period of time that is so is the validity of the general principle that less is more. except of course for sample size (Cohen & Cohen, 1983, pp ). I have encountered too many studies with prodigious numbers of dependent variables, or with what seemed to me far too many independent variables, or (heaven help us) both. In any given investigation that isn't explicitly exploratory, we should be studying few independent variables and even fewer dependent variables, for a variety of reasons. If all of the dependent variables are to be related to all of the independent variables by simple bivariate analyses or multiple regression, the number of hypothesis tests that will be performed willy-nilly is at least the product of the sizes of the two sets. Using the.05 level for many tests escalates the experimentwise Type I error rate or in plain English, greatly increases the chances of discovering things that aren't so. If, for example, you study 6 dependent and 10 independent variables and should find that your harvest yields 6 asterisks, you know full well that if there were no real associations in any of the 60 tests, the chance of getting one or more "significant" results is quite high (something like , which equals, coincidentally,.95), and that you would expect three spuriously significant results on the average. You then must ask yourself some embarrassing questions, such as, Well, which three are real?, or even. Is six significant significantly more than the chance-expected three? (It so happens that it isn't.) And of course, as you've probably discovered, you're not likely to solve your multiple tests problem with the Bonferroni maneuver. Dividing.05 by 60 sets a per-test significance criterion of.05/60 = , and therefore a critical two-sided t value of about 3.5. The effects you're dealing with may not be large enough to produce any interesting /s that high, unless you're lucky. Nor can you find salvation by doing six stepwise multiple regressions on the 10 independent variables. The amount of capitalization on chance that this entails is more than I know how to compute, but certainly more than would a simple harvest of asterisks for 60 regression coefficients (Wilkinson, 1990, p. 481). In short, the results of this humongous study are a muddle. There is no solution to your problem. You 1304 December 1990 American Psychologist Copyright 1990 bv the American Psychological Association. Inc. 0O03-066X/W$00.75 Vol. 45. No
2 wouldn't, of course, write up the study for publication as if the unproductive three quarters of your variables never existed.... The irony is that people who do studies like this often start off with some useful central idea that, if pursued modestly by means of a few highly targeted variables and hypotheses, would likely produce significant results. These could, if propriety or the consequences of early toilet training deemed it necessary, successfully withstand the challenge of a Bonferroni or other experimentwiseadjusted alpha procedure. A special case of the too-many-variables problem arises in multiple regression-correlation analysis with large numbers of independent variables. As the number of independent variables increases, the chances are that their redundancy in regard to criterion relevance also increases. Because redundancy increases the standard errors of partial regression and correlation coefficients and thus reduces their statistical significance, the results are likely to be zilch. I have so heavily emphasized the desirability of working with few variables and large sample sizes that some of my students have spread the rumor that my idea of the perfect study is one with 10,000 cases and no variables. They go too far. A less profound application of the less-is-more principle is to our habits of reporting numerical results. There are computer programs that report by default four, five, or even more decimal places for all numerical results. Their authors might well be excused because, for all the programmer knows, they may be used by atomic scientists. But we social scientists should know better than to report our results to so many places. What, pray, does an r mean? or, for an IQ distribution, a mean of ? For N = 100, the standard error of the r is about. 1 and the standard error of the IQ mean about 1.5. Thus, the 345 part of r = is only 3% of its standard error, and the 345 part of the IQ mean of is only 2% of its standard error. These superfluous decimal places are no better than random numbers. \They are actually worse than useless because the clutter they create, particularly in tables, serves to distract the eye and mind from the necessary comparisons among the meaningful leading digits. Less is indeed more here. Simple Is Better I've also learned that simple is better, which is a kind of loose generalization of less is more. The simple-is-better idea is widely applicable to the representation, analysis, and reporting of data. If, as the old cliche has it, a picture is worth a thou- This invited address was presented to the Division of Evaluation, Measurement, and Statistics (Division 5) at the 98th Annual Convention of the American Psychological Association in Boston, August 13, I am grateful for the comments on a draft provided by Patricia Cohen, Judith Rabkin, Raymond Katzell, and Donald F. Klein. Correspondence concerning this article should be addressed to Jacob Cohen, Department of Psychology, New York University, 6 Washington P!., 5th Floor, New York, NY sand words, in describing a distribution, a frequency polygon or, better still, a Tukey (1977, pp. 1-26) stem and leaf diagram is usually worth more than the first four moments, that is, the mean, standard deviation, skewness, and kurtosis. I do not question that the moments efficiently summarize the distribution or that they are useful in some analytic contexts. Statistics packages eagerly give them to us and we dutifully publish them, but they do not usually make it possible for most of us or most of the consumers of our products to see the distribution. They don't tell us, for example, that there are no cases between scores of 72 and 90, or that this score of 24 is somewhere in left field, or that there is a pile-up of scores of 9. These are the kinds of features of our data that we surely need to know about, and they become immediately evident with simple graphic representation. Graphic display is even more important in the case of bivariate data. Underlying each product-moment correlation coefficient in an acre of such coefficients there lies a simple scatter diagram that the r presumes to summarize, and well it might. That is, it does so if the joint distribution is more-or-less bivariate normal which means, among other things, that the relationship must be linear and that there are no wild outlying points. We know that least squares measures, like means and standard deviations, are sensitive to outliers. Well, Pearson correlations are even more so. About 15 years ago, Wainer and Thissen (1976) published a data set made up of the heights in inches and weights in pounds of 25 subjects, for which the r was a perfectly reasonable.83. But if an error in transcription were made so that the height and weight values for one of the 25 subjects were switched, the r would become -.26, a rather large and costly error! There is hardly any excuse for gaps, outliers, curvilinearity, or other pathology to exist in our data unbeknownst to us. The same computer statistics package with which we can do very complicated analyses like quasi-newtonian nonlinear estimation and multidimensional scaling with Guttman's coefficient of alienation also can give us simple scatter plots and stem and leaf diagrams with which we can see our data. A proper multiple regression/correlation analysis does not begin with a matrix of correlation coefficients, means, and standard deviations, but rather with a set of stem and leaf diagrams and scatter plots. We sometimes learn more from what we see than from what we compute; sometimes what we learn from what we see is that we shouldn't compute, at least not on those data as they stand. Computers are a blessing, but another of the things I have learned is that they are not an unmixed blessing. Forty years ago, before computers (B.C., that is), for my doctoral dissertation, I did three factor analyses on the I1 subtests of the Wechsler-Bellevue, with samples of 100 cases each of psychoneurotic, schizophrenic, and braindamaged patients. Working with a pad and pencil, 10-tothe-inch graph paper, a table of products of two-digit numbers, and a Friden electromechanical desk calculator that did square roots "automatically," the whole process took the better part of a year. Nowadays, on a desktop December 1990 American Psychologist 1305
3 computer, the job is done virtually in microseconds (or at least lickety-split). But another important difference between then and now is that the sheer laboriousness of the task assured that throughout the entire process I was in intimate contact with the data and their analysis. There was no chance that there were funny things about my data or intermediate results that I didn't know about, things that could vitiate my conclusions. I know that I sound my age, but don't get me wrong I love computers and revel in the ease with which data analysis is accomplished with a good interactive statistics package like SYSTAT and SYGRAPH (Wilkinson, 1990). I am, however, appalled by the fact that some publishers of statistics packages successfully hawk their wares with the pitch that it isn't necessary to understand statistics to use them. But the same package that makes it possible for an ignoramus to do a factor analysis with a pull-down menu and the click of a mouse also can greatly facilitate with awesome speed and efficiency the performance of simple and informative analyses. A prime example of the simple-is-better principle is found in the compositing of values. We are taught and teach our students that for purposes of predicting a criterion from a set of predictor variables, assuming for simplicity (and as the mathematicians say, "with no loss of generality"), that all variables are standardized, we achieve maximum linear prediction by doing a multiple regression analysis and forming a composite by weighting the predictor z scores by their betas. It can be shown as a mathematical necessity that with these betas as weights, the resulting composite generates a higher correlation with the criterion in the sample at hand than does a linear composite formed using any other weights. Yet as a practical matter, rnpst of the time, we are better off using unit weights: +1 for positively related predictors, -1 for negatively related predictors, and 0, that is, throw away poorly related predictors (Dawes, 1979; Wainer, 1976). The catch is that the betas come with guarantees to be better than the unit weights only for the sample on which they were determined. (It's almost like a TV set being guaranteed to work only in the store.) But the investigator is not interested in making predictions for that sample he or she knows the criterion values for those cases. The idea is to combine the predictors for maximal prediction for future samples. The reason the betas are not likely to be optimal for future samples is that they are likely to have large standard errors. For the typical 100 or 200 cases and 5 or 10 correlated predictors, the unit weights will work as well or better. Let me offer a concrete illustration to help make the point clear. A running example in our regression text (Cohen & Cohen, 1983) has for a sample of college faculty their salary estimated from four independent variables: years since PhD, sex (coded in the modern manner 1 for female and 0 for male), number of publications, and number of citations. The sample multiple correlation computes to.70. What we want to estimate is the correlation we would get if we used the sample beta weights in the population, the cross-validated multiple correlation, which unfortunately shrinks to a value smaller than the shrunken multiple correlation. For N = 100 cases, using Rozeboom's (1978) formula, that comes to.67. Not bad. But using unit weights, we do better:.69. With 300 or 400 cases, the increased sampling stability pushes up the cross-validated correlation, but it remains slightly smaller than the.69 value for unit weights. Increasing sample size to 500 or 600 will increase the cross-validated correlation in this example to the point at which it is larger than the unit-weighted.69, but only trivially, by a couple of points in the third decimal*. When sample size is only 50, the cross-validated multiple correlation is only.63, whereas the unit weighted correlation remains at.69. The sample size doesn't affect the unit weighted correlation because we don't estimate unstable regression coefficients. It is, of course, subject to sampling error, but so is the cross-validated multiple correlation. Now, unit weights will not always be as good or better than beta weights. For some relatively rare patterns of correlation (suppression is one), or when the betas vary greatly relative to their mean, or when the ratio of sample size to the number of predictors is as much as 30 to 1 and the multiple correlation is as large as.75, the beta weights may be better, but even in these rare circumstances, probably not much better. Furthermore, the unit weights work well outside the context of multiple regression where we have criterion data that is, in a situation in which we wish to measure some concept by combining indicators, or some abstract factor generated in a factor analysis. Unit weights on standardized scores are likely to be better for our purposes than the factor scores generated by the computer program, which are, after all, the fruits of a regression analysis for that sample of the variables on the factor as criterion. Consider that when we go to predict freshman grade point average from a 30-item test, we don't do a regression analysis to get the "optimal" weights with which to combine the item scores we just add them up, like Galton did. Simple is better. We are, however, not applying the simple-is-better principle when we "simplify" a multivalued graduated variable (like IQ, or number of children, or symptom severity) by cutting it somewhere along its span and making it into a dichotomy. This is sometimes done with a profession of modesty about the quality or accuracy of the variable, or to "simplify" the analysis. This is not an application, but rather a perversion of simple is better, because this practice is one of willful discarding of information. It has been shown that when you so mutilate a variable, you typically reduce its squared correlation with other variables by about 36% (Cohen, 1983). Don't do it. This kind of simplification is of a piece with the practice of "simplifying" a factorial design ANOVA by reducing all cell sizes to the size of the smallest by dropping cases. They are both ways of throwing away the most precious commodity we deal with: information. Rather more generally, I think I have begun to learn how to use statistics in the social sciences. The atmosphere that characterizes statistics as ap December 1990 American Psychologist
4 plied in the social and biomedical sciences is that of a secular religion (Salsburg, 1985), apparently of Judeo- Christian derivation, as it employs as its most powerful icon a six-pointed cross, often presented multiply for enhanced authority. I confess that I am an agnostic. The Fisherian Legacy When I began studying statistical inference, I was met with a surprise shared by many neophytes. I found that if, for example, I wanted to see whether poor kids estimated the size of coins to be bigger than did rich kids, after I gathered the data, I couldn't test this research hypothesis, but rather the null hypothesis that poor kids perceived coins to be the same size as did rich kids. This seemed kind of strange and backward to me, but I was rather quickly acculturated (or, if you like, converted, or perhaps brainwashed) to the Fisherian faith that science proceeds only through inductive inference and that inductive inference is achieved chiefly by rejecting null hypotheses, usually at the.05 level. (It wasn't until much later that I learned that the philosopher of science, Karl Popper, 1959, advocated the formulation of falsifiable research hypotheses and designing research that could falsify them.) The fact that Fisher's ideas quickly became the basis for statistical inference in the behavioral sciences is not surprising they were very attractive. They offered a deterministic scheme, mechanical and objective, independent of content, and led to clear-cut yes-no decisions. For years, nurtured on the psychological statistics textbooks of the 1940s and 1950s, I never dreamed that they were the source of bitter controversies (Gigerenzer & Murray, 1987). Take, for example, the yes-.no decision feature. It was quite appropriate to agronomy, which was where Fisher came from. The outcome of an experiment can quite properly be the decision to use this rather than that amount of manure or to plant this or that variety of wheat. But we do not deal in manure, at least not knowingly. Similarly, in other technologies for example, engineering quality control or education research is frequently designed to produce decisions. However, things are not quite so clearly decision-oriented in the development of scientific theories. Next, consider the sanctified (and sanctifying) magic.05 level. This basis for decision has played a remarkable role in the social sciences and in the lives of social scientists. In governing decisions about the status of null hypotheses, it came to determine decisions about the acceptance of doctoral dissertations and the granting of research funding, and about publication, promotion, and whether to have a baby just now. Its arbitrary unreasonable tyranny has led to data fudging of varying degrees of subtlety from grossly altering data to dropping cases where there "must have been" errors. The Null Hypothesis Tests Us We cannot charge R. A. Fisher with all of the sins of the last half century that have been committed in his name (or more often anonymously but as part of his legacy), but they deserve cataloging (Gigerenzer & Murray. 1987; Oakes, 1986). Over the years, 1 have learned not to make errors of the following kinds: When a Fisherian null hypothesis is rejected with an associated probability of, for example..026, it is not the case that the probability that the null hypothesis is true is.026 (or less than.05, or any other value we can specify). Given our framework of probability as long-run relative frequency as much as we might wish it to be otherwise this result does not tell us about the truth of the null hypothesis, given the data. (For this we have to go to Bayesian or likelihood statistics, in which probability is not relative frequency but degree of belief.) What it tells us is the probability of the data, given the truth of the null hypothesis which is not the same thing, as much as it may sound like it. If the p value with which we reject the Fisherian null hypothesis does not tell us the probability that the null hypothesis is true, it certainly cannot tell us anything about the probability that the research or alternate hypothesis is true. In fact, there is no alternate hypothesis in Fisher's scheme: Indeed, he violently opposed its inclusion by Neyman and Pearson. Despite widespread misconceptions to the contrary, the rejection of a given null hypothesis gives us no basis for estimating the probability that a replication of the research will again result in rejecting that null hypothesis. Of course, everyone knows that failure to reject the Fisherian null hypothesis does not warrant the conclusion that it is true. Fisher certainly knew and emphasized it, and our textbooks duly so instruct us. Yet how often do we read in the discussion and conclusions of articles now appearing in our most prestigious journals that "there is no difference" or "no relationship"? (This is 40 years after mytv = 20 friend used a nonsignificant result to demolish psychoanalytic theory.) The other side of this coin is the interpretation that accompanies results that surmount the.05 barrier and achieve the state of grace of "statistical significance." "Everyone" knows that all this means is that the effect is not nil, and nothing more. Yet how often do we see such a result to be taken to mean, at least implicitly, that the effect is significant, that is, important, large. If a result is highly significant, say p <.001, the temptation to make this misinterpretation becomes all but irresistible. Let's take a close look at this null hypothesis the fulcrum of the Fisherian scheme that we so earnestly seek to negate. A null hypothesis is any precise statement about a state of affairs in a population, usually the value of a parameter, frequently zero. It is called a "null" hypothesis because the strategy is to nullify it or because it means "nothing doing." Thus, "The difference in the mean scores of U.S. men and women on an Attitude Toward the U.N. scale is zero" is a null hypothesis. "The product-moment r between height and IQ in high school students is zero" is another. "The proportion of men in a population of adult dyslexics is.50" is yet another. Each is a precise statement for example, if the population r December 1990 American Psychologist 1307
5 between height and IQ is in fact.03, the null hypothesis that it is zero is false. It is also false if the r is.01,.001, or ! A little thought reveals a fact widely understood among statisticians: The null hypothesis, taken literally (and that's the only way you can take it in formal hypothesis testing), is always false in the real world. It can only be true in the bowels of a computer processor running a Monte Carlo study (and even then a stray electron may make it false). If it is false, even to a tiny degree, it must be the case that a large enough sample will produce a significant result and lead to its rejection. So if the null hypothesis is always false, what's the big deal about rejecting it? Another problem that bothered me was the asymmetry of the Fisherian scheme: If your test exceeded a critical value, you could conclude, subject to the alpha risk, that your null was false, but if you fell short of that critical value, you couldn't conclude that the null was true. In fact, all you could conclude is that you couldn 't conclude that the null was false. In other words, you could hardly conclude anything. And yet another problem I had was that if the null were false, it had to be false to some degree. It had to make a difference whether the population mean difference was 5 or 50, or whether the population correlation was.10 or.30, and this was not taken into account in the prevailing method. I had stumbled onto something that I learned after awhile was one of the bases of the Ney man- Pearson critique of Fisher's system of statistical induction. In 1928 (when I was in kindergarten), Jerzy Ney man and Karl Pearson's boy Egon began publishing papers that offered a rather different perspective on statistical inference (Neyman & Pearson, 1928a, 1928b). Among other things, they argued that rather than having a single hypothesis that one either rejected or not, things could be so organized that one could choose between two hypotheses, one of which could be the null hypothesis and the other an alternate hypothesis. One could attach to the precisely denned null an alpha risk, and to the equally precisely defined alternate hypothesis a beta risk. The rejection of the null hypotheses when it was true was an error of the first kind, controlled by the alpha criterion, but the failure to reject it when the alternate hypothesis was true was also an error, an error of the second kind, which could be controlled to occur at a rate beta. Thus, given the magnitude of the difference between the null and the alternate (that is, given the hypothetical population effect size), and setting values for alpha and beta, one could determine the sample size necessary to meet these conditions. Or, with the effect size, alpha, and the sample size set, one could determine the beta, or its complement, the probability of rejecting the null hypothesis, the power of the test. Now, R. A. Fisher was undoubtedly the greatest statistician of this century, rightly called "the father of modern statistics," but he had a blind spot. Moreover, he was a stubborn and frequently vicious intellectual opponent. A feud with Karl Pearson had kept Fisher's papers out of Biometrika, which Karl Pearson edited. After old-man Pearson retired, efforts by Egon Pearson and Neyman to avoid battling with Fisher were to no avail. Fisher wrote that they were like Russians who thought that "pure science" should be "geared to technological performance" as "in a five-year plan." He once led off the discussion on a paper by Neyman at the Royal Statistical Society by saying that Neyman should have chosen a topic "on which he could speak with authority" (Gigerenzer & Murray, 1987, p. 17). Fisher fiercely condemned the Neyman-Pearson heresy. I was of course aware of none of this. The statistics texts on which I was raised and their later editions to which I repeatedly turned in the 1950s and 1960s presented null hypothesis testing a la Fisher as a done deal, as the way to do statistical inference. The ideas of Neyman and Pearson were barely or not at all mentioned, or dismissed as too complicated. When I finally stumbled onto power analysis, and managed to overcome the handicap of a background with no working math beyond high school algebra (to say nothing of mathematical statistics), it was as if I had died and gone to heaven. After I learned what noncentral distributions were and figured out that it was important to decompose noncentrality parameters into their constituents of effect size and sample size, I realized that I had a framework for hypothesis testing that had four parameters: the alpha significance criterion, the sample size, the population effect size, and the power of the test. For any statistical test, any one of these was a function of the other three. This meant, for example, that for a significance test of a product-moment correlation, using a twosided.05 alpha criterion and a sample size of 50 cases, if the population correlation is.30, my long-run probability of rejecting the null hypothesis and finding the sample correlation to be significant was.57, a coin flip. As another example, for the same a =.05 and population r =.30, if I want to have.80 power. I could determine that I needed a sample size of 85. Playing with this new toy (and with a small grant from the National Institute of Mental Health) I did what came to be called a meta-analysis of the articles in the 1960 volume of the Journal ofabnormal and Social Psychology (Cohen, 1962). I found, among other things, that using the nondirectional.05 criterion, the median power to detect a medium effect was.46 a rather abysmal result. Of course, investigators could not have known how underpowered their research was, as their training had not prepared them to know anything about power, let alone how to use it in research planning. One might think that after 1969, when I published my power handbook that made power analysis as easy as falling off a log, the concepts and methods of power analysis would be taken to the hearts of null hypothesis testers. So one might think. (Stay tuned.) Among the less obvious benefits of power analysis was that it made it possible to "prove" null hypotheses. Of course, as I've already noted, everyone knows that one can't actually prove null hypotheses. But when an inves December 1990 American Psychologist
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text:
Where Do You Stand? Telling Your Ex What He Did Wrong
Even if ending a relationship is a relief, leaving a person or being left by someone can come with feelings of sadness and longing, which is probably why so many couples hook up after they've broken up. But there is a flipside to this: some people aren't ready to let go of their bad feelings.
A friend of mine, in post-breakup mode, says every day is a battle not to call her ex. But not because she misses him and wants to reconnect, but because she wants to finally let him have it. She's angry and ready to tell him exactly why. I think her feelings are natural, and though I understand her desire, I don't think it's a good idea. But what do you think? Where do you stand when it comes to offering your ex some brutal honesty in an effort to make yourself feel better?


text:
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Torii Hunter Jr. gave college football a try. Now the Notre Dame receiver will try to follow in his father's career path by playing baseball.
Hunter, the son of former Twins, Angels and Tigers outfielder Torii Hunter, announced Thursday he will forgo his final year of football eligibility to pursue a baseball career. The Angels drafted Hunter in the 23rd round in June.
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Hunter, selected as a football captain this season, was plagued by injuries throughout his career, including a concussion he suffered during the 2016 opener. He also had a knee injury...


text: First of all, Ben isn't dead. However, there is an episode in two weeks that features ben called "Dead is Dead", so think of that what you will. Remember when sayid said, "That's what I am here to do" when talking about escaping with the young ben linus because he thinks that sayid is a "hostile" and he wants to join the hostiles. Now, if the others are the original inhabitants of the island, which goes back to when Richard Alpert got rid of the army troup trying to test an H-bomb and ultimately purged the Dharma Initiative. Bear in mind, the Dharma Initiative comes from the Hanso Foundation which is associated with the Widmore Corporation. If you think that the Island in some way has a plan for people or a destiny, which I do, then it naturally wouldn't like the Dharma Initiative's presence. These are outsiders coming in. Darma only came in during the 1970's and I'm guessing that Charles Widmore's organization had some indirect association or funding of the dharma initiative's cause, whatever that may be (curing cancer, testing the valenzetti equation, psychological/telepethy tests). Bear in mind that Widmore was kicked off the island for whatever reason when he used to be a memeber of the "original inhabitents" lead be richard alpert, and not allowed to return, ben wasn't. I'm not saying that the Dharma Initiative is bad. Alvar Hanso, the creator of the Dharma Initiative as seen in the Sri Lanka video, seems to be generally good and interested in "mental health appeal" and the general concept of "good people". All the Dharma Initiatve people seemed to have been recruited for their caliber and "goodness", this is the same with the others. But to me, I feel that Richard Alpert is some kind of vessle for the island. He seems to know probably the most about the island's history and true intentions than anybody else. And he believed in Ben and was part of appointing ben to take over for him as the new leader of the island's "original inhabitants". Now, John Locke is the new leader. So, if you think that Ben is potentially a "bad" guy, he was chosen just like Locke to be the leader and carry out the prophecies of the island. One theory could be that it was meant for Sayid to kill young Ben because Ben needed to die possibly because the prophecy of him was wrong and that he is evil. I don't know if I agree with this. Also, Ben was given a fatal tumor by the island and possibly forecasted to die. Right now, I think ben is essentially a good guy. He was the one who told Locke that he had to bring the oceanic six back, which was truth. This was seconded to Locke by Christian Sheppard, who also seems to be somehow all-knowing and a "vessel" of the island. So ben is folliwing the prophecies of the island/the others (who/whatever is commanding these). When ben killed locke in that room, it may have been to assure himself that he was dead, which he had to be (Christian, Alpert said so). Ben may have also been involved in getting Sayid and Hugo on the plane, both of whom show up on the Ajira Airlines flight somewhat mysteriously, also Frank is the pilot. I don't think that was an element of fate, someone organized Hugo and Sayid to be on the plane, someone working with ben, maybe Eloise Hawiking maybe someone else. Also, who were all those people ben had Sayid killing? I don't think that they were Widmore's people. Widmore never had any intention of killing any of the surviors of the flight and it's been said that he wasn't the one to put the fake plane/bodies in that trench. They could have been Widmore's people, but ben needed to persuade Sayid to kill them so he said that they were out to kill them, that they had a guy outside's Hurley's mental hospital, etc... I don't really have a solid theory on what Ben's intentions were here. It could have been that he needed Sayid to kill these people so that he could get caught by a bounty hunter and ultimately taken to the Ajira Airlines flight. For whatever reason all of the Oceanic Six are important to the island and had to come back in order to stop the constant movement through time of the island, also it was important that jack, kate, and hurley rejoin Sawyer and Juliette in Dharma times. So, I do not think right now that young ben was killed by sayid, but i could be wrong also how does one figure if the young ben's death would immedietly affect the older ben, who is also on the island. If I had to say, i would say that if young ben was killed, his older self would be dead too. My guess is we don't find out till 2 weeks in the episode "Dead is Dead" which features ben. Also, there's the question of what did ben in the immidete time before boarding the Ajira Airlines flight. I do think he went to kill Penelope Widmore. He promised to avenge his daughter's death by killing Widmore's daughter, although for some reason he cannot kill Charles Widmore directly. Desmond may have gotten involved and been killed as well in the skirmish which injured ben. We haven't seen any desmond since he left that church or whatever where Eloise Hawking was, that was right before ben told jack that he had to go take care of a loose end before he went back to the island. So, for whatever reason there is a major rivalry between Ben and Widmore, Widmore claims to have led the others for 30 years before being kicked out, this would have been around the time Ben took power after the "purge" of the Dharma Initiative. Ben and Ethan, possibly others, left the Dharma Initiative where they came from and joined the others. Widmore was a memeber of the others and may have been born on the island, his father created the Widmore corporation in the mid 1800's was a trader of sorts and was responsible for having the Black Rock land on the island. So, one could argue that Widmore was born on the island. SO, it may have been "the island's" decision, or Alpert's, to kick off Widmore, who seemed to be a bit of a hothead as a youngster and the leadership was given to ben in the mid 70's. Widmore may have led the others between the late 50's - late 70's or so. Some theories: 1. The island may have wanted ben dead in order to have the Dharma Initiative remain in existence, 2. The island didn't want the Dharma Initiative there and that's why Ben couldn't have died as a kid. 3. Ben could talk to Jacob and was a very large element of what the island did, so therefore he couldn't have been killed by Sayid's rash decision to shoot him in the heat of the moment because Sayid wanted revenge on Linus for what he put him through. All I know and believe to be the truth is that Richard Alpert is good and he has been carrying out the prophecies of the island for a long time. He got rid of the army, he found locke, ben, He got rid of the dharma initiative. He told locke to die and bring back his friends. So, I think Ben is an element of good even though he always has a look like he's got something planned and is up to no good. Although ben has his vices, he kills people without any issue. So, think what you will and respond to this blog post, and we'll think about some more theories about what is/isn't true/good.