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10 Things Teens Won't Tell You Leave a comment
10 things teens won't tell you
By Quentin Fottrell
Published: Aug 16, 2014 8:11 a.m. ET
The secret and costly life of the American teenager
Chip Wass
1. America will look a lot different when we grow up.
Like every generation of adolescents, today's teens have habits that are utterly unfamiliar to their parents. The roughly 25 million Americans between the ages of 13 and 18 grew up with Facebook and Netflix. They're more likely to hang out at Chipotle and Starbucks, and less likely to hang out at the mall, than teens of 20 or even 10 years ago.
But teens' attitudes are also being shaped by an era where people are less likely to assume that a "typical" American family is straight and white. "They're the most socially and ethnically diverse of all generations," says Sharalyn Hartwell, executive director at consulting firm Frank N. Magid Associates, which studies teenage demographics. While their parents saw Morgan Freeman as the U.S. president in the 1998 movie "Deep Impact," modern teens grew up with the real-life Obama White House.
Ready to live in a nontraditional nation.
Only 55% of Americans 18 and under are Caucasian, compared with 72% of baby boomers, according to Magid. Not coincidentally, teenagers and "tweens" are more comfortable with the country's changing ethnic balance. Some 47% say they feel positively about the U.S. becoming more ethnically diverse, compared with 32% of boomers.
Teenagers are also growing up in a society where same-sex marriage is more widely accepted, and, as a result, television shows aimed at their age group reflect this new reality, she adds. Disney's "Good Luck Charlie" featured a same-sex couple, ABC Family's "The Fosters" is a television show about a family with two moms, and ABC's prime-time comedy "Modern Family" features a male same-sex couple that wrapped its last season with a wedding.
Today's teens and tweens are also more likely than earlier generations to be the products of a particularly hands-on style of parenting—one that involves 24/7 online monitoring and more involvement in their education. Demographers and researchers say that such tighter-knit parenting can have an impact on how these teens will perceive the world as they become adults: They'll be more likely to be realistic about their future and to embrace change—though if the parenting was too claustrophobic or authoritarian, they'll also be more likely to be rebellious and get along poorly with others.
Also read: 10 things Generation X won't tell you.
2. We're one click ahead of you online
Some 95% of teenagers are online compared with around 80% of the overall population, according to the Pew Internet and American Life Project. In theory, that should make it easier for parents to keep up with them and track their behavior. But teens are light on their feet, and data suggests that teens are quietly fleeing mainstream social sites that have been adopted by their parents
During 2013, the share of teens active on Facebook dropped by 9 percentage points, while on Twitter it dropped 3 points, according to research firm GlobalWebIndex. Teens are gravitating instead to services like Blink and Snapchat, where messages are easier to keep private. "Video apps like Instagram and Vine are also playing a much larger role with this generation," adds Jeanne Connon, chief marketing officer of FPgirl.com, a marketing firm that analyzes fashion, technology, trends and relationships among young girls. What's more, teens are adept at hiding apps in folders on their computers or mobile devices to make them more difficult to find.
Parents are doing their best to play catch-up, Connon says, but it's still an uphill battle. Around 1 in 5 tweens and young teenagers in middle school have received a sexually explicit message or photo, according to a survey of 1,200 middle school students published in July in the "American Academy of Pediatrics." Those who received such a text were also six times more likely to be sexually active.
Also see: Teens rebel against Facebook.
3. We're sooo bored…with the shopping mall
Teen-oriented retailers, take note: Shopping may be losing its mystique among the under -18 set. The latest retailing survey by investment bank Piper Jaffray found that the average teenager spent $1,000 on fashion annually, down from $1,300 in 2006, and took 29 shopping trips a year, down from 38 in 2007. For the first time in the survey's 13-year history, they spent a bigger share of their spending money on food than clothing (20.8% versus 20.7%).
Online shopping may make the teenage mall-rat an endangered species.
The big issue here is that teenagers are shopping with their tablets and smartphones, rather than in person: 75% of teenage girls and 50% of teenage boys says they prefer shopping online than in-store. (They spend an average of $56.50 per shopping trip when they do make it to a brick-and-mortar store.) "Teens are browsing regularly on their mobile devices, shopping less frequently and engaging with brands on demand," says Steph Wissink, senior research analyst at Piper Jaffray.
Teens' spending may be waning because their parents are economizing: They remain heavily dependent on the fortunes of their parents, who contribute around 65% of their annual spending, according to the report. And while they do have a penchant for expensive clothing brands, there are only one or two that have a firm hold over them: 19% of male teenagers prefer Nike and the same percentage of female teenagers prefer Lululemon leggings.
4. We do drugs (but not the same ones you did)
Around 36% of high-school students report having used marijuana at least once within the previous 12 months, according to data released in July the Partnership for Drug-Free Kids. That's down from 41% in 1998. The share of teenagers who used alcohol over the past year has declined more sharply, to 51% in 2013 from 68% in 1998.
Marijuana remains popular with teens, but prescription drug abuse is rising faster.
Use of other drugs, however, have risen slightly: 23% of teens admit to abusing or misusing prescription drugs, at least once in their lifetime, up from 20% five years ago, and one in six report doing so within the past year. Pain medications like Oxycodone and Vicodin and "study drugs" that combat attention-deficit-disorder are among the most commonly abused, according to Partnership for Drug-Free Kids. And abuse of human growth hormone (HGH)—often used by athletes seeking to add muscle—has risen among teens to 11% in 2013 from 5% a year earlier.
"They are not doing [these drugs] to get high, they're doing it because they think they can stay up studying to get better grades, to relax and get fit," says Steve Pasierb, president and CEO of the Partnership for Drug-Free Kids.
Also see: Cocaine use is going to pot.
5. We can't do financial math (but neither can you)
American teens don't fare so well on the "Program for International Student Assessment," an international survey of financial literacy conducted by the Organization for Economic Cooperation and Development every three years. The most recent version, released in July, tested more than 510,000 15-year-olds across 18 countries. Americans scored below average (492 points versus an OECD average of 500 points), finishing behind China (No. 1 with an average of 603 points), Belgium (541), Estonia (529), Australia (526 points) and New Zealand (520).
Why Johnny can't understand derivatives
Can the U.S. improve financial literacy? We ask Education Secretary Arne Duncan.
Experts blame the system—not the students. "Teenagers don't know about financial literacy because adults have a low level of financial literacy too," says AnnaMaria Lusardi, a professor of economics and accountancy at George Washington University School of Business. In a 2011 study conducted by Lusardi, only 30% of U.S. adults gave correct answers to three basic questions concerning numeracy, inflation and risk diversification (versus 53% in Germany and 45% in the Netherlands). Nonetheless, 62% of teens say their parents are good financial role models, according to a survey by tax preparers H&R Block.
Some states are making efforts to fill this knowledge gap, introducing more economics and personal finance classes. For the first time, all 50 states and D.C. now include economics in their K-12 standards, according to the nonprofit, nonpartisan Council of Economic Education. Still, only 22 states require students to take an economics course as a high school graduation prerequisite, and only six states require the testing of student knowledge in personal finance.
6. Your recession-era stress is contagious
Teens report having stress levels that surpass that of their parents during the school year, a recent survey by the American Psychological Association found. Teens reported stress levels of 5.8 on a 10-point scale, compared with 5.1 for adults; those levels declined to 4.6 for teens during the summer, but that still ranked above the 3.9 score that's considered normal. One-third of teens reported symptoms of fatigue related to their stress, more than one-quarter said they skipped meals, and some 30% said they felt overwhelmed or depressed.
School is a drag; parental stress, even more so.
Nearly 40% of parents say their high school kids experience stress, according to a 2013 Harvard School of Public Health Survey conducted for National Public Radio. About one-quarter of high-school students' parents said homework caused their child a lot of stress, the survey found.
But school is far from the only factor, experts say. Teens pick up coping mechanisms or a lack thereof from their parents, and they're also more likely to experience parental divorce than children were 20 years ago, says Lynn Bufka, assistant executive director for practice research and policy at the American Psychological Association. The impact of stress over divorce and financial trouble gets passed onto children who may also feel less able to tell their parents or teachers that they're feeling stressed out, she says. "Often times, they don't want to add to their parents' burden," Bufka says. If a child has a problem turning in homework or lack of attention, she adds, parents should tell teachers what's going on at home.
One potentially positive sign for college-bound teens: The SAT will undergo a revamp in 2016 in ways the experts say will make it somewhat less demanding.
7. Our hunger for gadgets will cost you billions
Remember when loose-leaf binders and a new backpack were all the school supplies you needed? Parents are set to spend $8.4 billion on back-to-school electronics this year, including computers, tablets and smartphones, up 7% from last year, according to the National Retail Federation. Back-to-school shoppers will spend an average $212.35 per household on electronic items.
Stanislav Komogorov/Shutterstock
Phones are a major driver of this spending: About 27% of teens owned smartphones last year, up from 23% in 2011. Howard Schaffer, vice president of retailing website Offers.com, says these mobile devices are seen as critical by parents because they allow them to keep tabs on their kids via geolocation apps like "Trick or Tracker" or "Connect."
Some parents say they're trying to moderate the back-to-school tech spending this year. Only 37% of parents are buying tablets or computers, compared with 61% who will underwrite clothing and 55% who will buy shoes for their kids, according to a survey of 1,000 parents by Offers.com. And 36% say they'll spend more than $200 per child on technology, down from 45% in 2013; Schaffer says that reflects the fact that computers and smartphones are lasting longer.
Also see: 5 apps for spying on your spouse.
8. We'll double the price of your car insurance
Adding a teenage driver to a married couple's car insurance can be a financial tsunami for the typical family. According to a report by insuranceQuotes.com, a division of personal finance site Bankrate.com, adding a male teenager hikes premiums by an average of 92%; while female teen drivers hike premiums by 67%. The good news for parents: The older their child becomes, the lower the premium. The premium hike falls from 96% for 16-year-olds to 58% for 19-year-olds.
Texting and driving: Bad at any age, particularly troublesome among teens.
The most expensive state to insure a teenage driver is New Hampshire, where the average premium surged 111%. Hawaii is the only state that prohibits age, gender and length of driving experience from affecting car insurance costs, so teen drivers there cost only 17% more to insure on their parents policy.
That said, more families are dodging this bill these days. There are fewer teens on the road, says Laura Adams, senior insurance analyst at BankRateInsurance.com. Twenty years ago, 70% of 18-year-olds had their driver's license, she says, but today only 54% do, due in part to the rising cost of car ownership, increased unemployment among teens, and the increased use of social media (rather than face-to-face time) for teenage socializing.
There are other ways to minimize the financial damage: For example, some cars are cheaper to insure than others. "Choose a model that has a low crash history," says Russ Rader, spokesman for the Highway Loss Data Institute, which publishes insurance claim data. Larger, heavier family cars such as Toyota Corollas, Ford sedans or Subarus are safer for teens, and their insurance rates reflect that. Many insurance companies offer a "good student" discount for those with B averages or better, adds Phil Reed, senior consumer advice editor at Edmunds.com. "A friend of mine told his teen driver that if he got a B average, he would split his insurance savings with him," he says. "It was good motivation and a gain for both of them."
9. We get bullied, even when we're popular
Teenage movies from "Clueless" (1995) to "Mean Girls" (2004) show the hazards of being on the low end of the status scale at high school. But a recent study offers evidence that popular kids get bullied, too.
Being popular doesn't mean they're safe.
For the paper "Causality of Social Combat: School Networks of Peer Victimization and Their Consequences" (links to pdf), researchers at the University of California and Pennsylvania State University studied 4,200 high-school students across 19 schools. They found that students' chances of being bullied rise 25% if they move from a group with average popularity to the 95th percentile, beyond which victimization begins to drop. (Popularity was measured based on friendship nominations among students.) Those with higher social status also experienced stronger adverse psychological consequences when bullied—because they felt like they had more to lose. More popular kids may also escape the radar of concerned educators and parents who focus on isolated students. The study also found that females are victimized 30% more often than males and "social isolates" are bullied 23% more often than others.
There has been a major push to combat bullying in recent years, including suicide prevention campaigns such as the LGBT-focused "It gets better" social media effort, which featured videos from sports stars, celebrities and politicians, including President Obama. In 2012, the Department of Education released a free training tool kit aimed at reducing bullying in schools. But 17% of students report being bullied 2 to 3 times a month during a school semester, and 1 in 10 drop out because of bullying, according to DoSomething.org, a nonprofit organization focused on young people and social change.
10. We don't buy into the American Dream
Like member of other generations, most teens define the American Dream as involving homeownership, educational opportunities, a high standard of living and the likelihood of doing better than the previous generation. But teens are more likely to see that Dream as out of reach. According to a survey by Magid Associates, only 60% of teens and tweens believe in the American Dream compared with 71% of millennials, 64% of Generation Xers and 75% of boomers.
Although most teens are too young to remember 9/11, they're not too young to recall how their parents struggled during the recession. "They're influenced by their Gen X parents and older siblings," Hartwell says. Many Xers were clobbered financially by the housing bubble, while millennials have more college debt than any previous generation of Americans. Older role models are "telling these kids that it's not going to be better for you just because it should be."
But that doesn't necessarily make teens negative or pessimistic, it makes them pragmatic and realistic. "They still have the optimism of youth," Hartwell says.
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Posted August 19, 2014 by sotpyouth in Bullying, Drugs/Alcohol, Family, General, Main, Media, School, Sports, Technology
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\section{\label{Section:Introduction}Introduction}
Weak-scale supersymmetry (SUSY)\cite{Salam:1974yz,Haber:1984rc,Nilles:1983ge,Martin:1997ns} was supposed to solve the naturalness problem of the Standard Model (SM)\cite{Gildener:1976ai,Susskind:1978ms}, but it was absent in the ATLAS\cite{ATLAS-CONF-2013-047} and CMS\cite{Chatrchyan:2014lfa} searches at the Large Hadron Collider (LHC) in $20\invfb$ with center-of-mass energies of \roots{7} and \roots{8}. Although ATLAS and CMS will continue their searches for SUSY at \roots{13}, a new \roots{100} Very Large Hadron Collider (VLHC) might be built\cite{FCC}.

There are numerous motivations for SUSY. The theoretical motivations for SUSY (see \eg \refcite{Dine:2007zp}) are, \textit{inter alia,} that it completes the maximal symmetries of the $S$-matrix and connects with gravity and superstrings. The phenomenological and experimental motivations for SUSY (see \eg \refcite{Baer:2006rs}) are that it unifies the gauge couplings at the anticipated scale, that the lightest SUSY particle could explain the measured abundance of dark matter in the Universe and that it predicts that the mass of the lightest Higgs boson is $\mh\lesssim135\gev$. Perhaps the strongest motivation for SUSY, however, is that it solves the technical naturalness problem of the SM, if SUSY particles are sufficiently light. The LHC results, however, suggest that SUSY particles might not be sufficiently light\cite{Giudice:2013yca,Feng:2013pwa} and have lead many to question naturalness arguments\cite{Foot:2013hna,Dubovsky:2013ira,Heikinheimo:2013fta,Farina:2013mla,deGouvea:2014xba}.

We argue in \refsec{Section:Evidence} that the best measure of naturalness is Bayesian evidence and measure naturalness in the SM in \refsec{Section:SM_evidence} and  in the Constrained Minimal Supersymmetric SM (CMSSM)\cite{Chamseddine:1982jx,Arnowitt:1992aq,Kane:1993td} in \refsec{Section:CMSSM_evidence} by calculating their Bayesian evidences with \ic{honest} or \ic{naturalness} priors. We evaluate the consequences for naturalness of hypothetical null results from a \roots{100} VLHC with Bayesian statistics, \ie the \ic{fine-tuning price} of the VLHC\cite{Chankowski:1997zh,Barbieri:1998uv,Strumia:2011dv}, by calculating the Bayesian evidence in this scenario. Learning this \ic{price} could motivate building the VLHC\cite{nima}. We argue that our comparison between the SM and the CMSSM was fair in \refsec{Section:Fair_Comp}. We discuss the $\mu$-problem of the MSSM\cite{Kim:1983dt} in the context of Bayesian statistics in \refsec{Section:Mu_problem}, and conclude in \refsec{Section:Conclusions}.  For similar analyses, see \eg \refcite{Giusti:1998gz,Allanach:2006jc,Allanach:2007qk,Feroz:2008wr,Cabrera:2012vu,Kim:2013uxa,Balazs:2013qva}.

\section{\label{Section:Evidence}Bayesian evidence}
For a pedagogical introduction to Bayesian statistics, see \eg \refcite{Trotta:2008qt}.  In Bayesian statistics, probability is a numerical measure of belief in a proposition. With Bayes' theorem, our belief in a model given experimental data is given by
\begin{equation}
\label{Eqn:Bayes_Theorem}
\pg{\model}{\data} = \frac{\pg{\data}{\model} \times \p{\model}}{\p{\data}},
 \end{equation}
where $\ev\equiv\pg{\data}{\model}$ is the \textit{Bayesian evidence,} $\p{\model}$ is our prior belief in the model, and $\p{\data}$ is a normalization constant.
We can eliminate the normalization constant if we consider a ratio of probabilities for $\model_a$ and $\model_b$;
\begin{equation}
\label{Eqn:Bayes_Factor}
\underbrace{\frac{\pg{\model_a}{\data} }{ \pg{\model_b}{\data}}}_{\text{Posterior odds, }\theta^\prime} = \underbrace{\frac{\pg{\data}{\model_a} }{ \pg{\data}{\model_b}}}_{\text{Bayes-factor, }B} \times \underbrace{\frac{ \p{\model_a} }{\p{\model_b}}}_{\text{Prior odds, }\theta}.\\
 \end{equation}
Our \textit{prior odds}, $\theta$, is a numerical measure of our relative belief in $\model_a$ over $\model_b$, \textit{before} considering experimental data.
The Bayes-factor, $B$, updates our \textit{prior odds,} $\theta$, with the experimental data, resulting in our \textit{posterior odds,} $\theta^\prime$. Our \textit{posterior odds}  is a numerical measure of our relative belief in $\model_a$ over $\model_b$, \textit{after} considering experimental data. The Bayes-factor is the ratio of the models' evidences.

Let us make our discussion more concrete. From an experiment, one can construct a ``likelihood function'' giving the frequentist probability of obtaining the data, given a particular point, \point, in a model's parameter space,
\begin{equation}
\likef{\point} = \pg{\data}{\point,\model}.
 \end{equation}
The likelihood function for a measurement is typically a Gaussian function (by the central limit theorem). It could be, \eg the probability of measuring a Higgs mass $\mh=125\gev$ given a particular parameter point \point in a SUSY model. With Bayes' theorem, it can be readily  shown that the evidence is an integral over the likelihood,
\begin{equation}
\label{Eqn:Evidence}
\ev = \int \likef{\point} \priorf{\point} \pd x,
 \end{equation}
where $\priorf{\point}\equiv\pg{\point}{\model}$ is our \textit{prior}; our prior belief in the model's parameter space. Priors are somewhat subjective and there might exist a spectrum of assigned priors amongst investigators. All investigators, however, will make identical conclusions from the evidence, if the likelihood is sufficiently informative.

Because individual evidences are somewhat meaningless (\eg the evidence has dimension $[1/\data]$), it is necessary to compare an evidence against that of a reference model with a Bayes-factor. If the Bayes-factor is greater than (less than) one, the model in the numerator (denominator) is favored. The interpretation of Bayes-factors is somewhat subjective, though we have chosen the Jeffreys' scale, \reftable{Table:Jefferys_Scale}, to ascribe qualitative meanings to Bayes-factors.  If a Bayes-factor is sufficiently large, all investigators will conclude that a particular model is favorable, regardless of their prior odds for the models. The Jeffreys' scale is, however, only a guide for interpreting a Bayes-factor; the full result is the posterior odds found by multiplying the Bayes-factor by the prior odds in \refeq{Eqn:Bayes_Factor}.

\begin{table}[ht]
\centering
\begin{ruledtabular}
\begin{tabular}{llll}
Grade   & Bayes-factor, $B$             & Preference for model in numerator\\
\hline
0       & $B\le1$                                       &   Negative\\
1       & $1<B\le3$                               &  Barely worth mentioning\\
2       & $3<B\le20$                            &  Positive\\
3       & $20<B\le150$                       &  Strong\\
4       & $B>150$                                 &  Very strong\\
\end{tabular}
\end{ruledtabular}
\caption{The Jeffreys' scale for interpreting Bayes-factors\cite{nla.cat-vn759870}, which are ratios of evidences. We assume that the favored model is in the numerator, though this could be readily inverted.}
\label{Table:Jefferys_Scale}
\end{table}

The Bayes-factor quantitatively incorporates a principle of economy widely-known as Occam's razor and in physics as \ic{fine-tuning} or \ic{naturalness}\cite{2009arXiv0903.4055G,Barbieri:1987fn,Ellis:1986yg}. It is insightful to consider the evidence $\ev = \pg{\data}{\model}$ a function of the data normalized to unity, \ie as a sampling distribution\cite{MacKay91}.  Natural models \ic{spend} their probability mass near the obtained  data, \ie a large fraction of their parameter space agrees with the data. Complicated models squander their probability mass away from the obtained data. This is illustrated in \reffig{Fig:EvidenceExample}. Bayesian statistics formalizes Occam's razor, fine-tuning and naturalness arguments. Naturalness is no longer a nebulous, aesthetic criterion; it is formalized and justified by Bayesian statistics.

\begin{figure}[ht]
\centering
\includegraphics[height=0.49\linewidth]{EvidenceExample.pdf}
\caption{Illustration of the evidence, interpreted as a sampling distribution, originally from \refcite{MacKay91}. The observed evidence is the evidence evaluated at the observed data. The red line shows a model that concentrates its probability mass at the observed data: it is a good, simple model. The green line shows a model that concentrates its probability mass away from the observed data: it is a bad, simple model. The blue line shows a model that thinly spreads its probability mass around the observed data: it is an OK, complicated model.
}
\label{Fig:EvidenceExample}
\end{figure}

We measure the \ic{fine-tuning price} of new experimental data with a partial Bayes-factor. A partial Bayes-factor, $P$, updates our relative belief in $\model_a$ over $\model_b$ with new experimental data,
\begin{equation}
P \cdot \frac{\pg{\model_a}{\data} }{ \pg{\model_b}{\data}} = \frac{\pg{\model_a}{\data + \text{new \data}} }{ \pg{\model_b}{\data + \text{new \data}}}.
 \end{equation}
It can be readily shown that a partial Bayes-factor is a ratio of Bayes-factors,
\begin{equation}
P =  \frac{\pg{\data + \text{new \data}}{\model_a} }{ \pg{\data + \text{new \data}}{\model_b}}  \frac{ \pg{\data}{\model_b}}{\pg{\data}{\model_a} }.
 \end{equation}
See \eg \refcite{Balazs:2013qva} for a comprehensive discussion of partial Bayes-factors. Having introduced our formalism, we are ready to calculate evidences in the SM and CMSSM.

\section{\label{Section:SM_evidence} Bayesian evidence for the Standard Model}
If the SM is coupled to the Planck scale,  it suffers from a well-known fine-tuning problem, the \ic{hierarchy problem}\cite{Gildener:1976ai,Susskind:1978ms}. The dimension-two coupling, $\mu^2$, in the Higgs potential,
\begin{equation}
V = \mu^2 \phi^2 + \lambda \phi^4,
 \end{equation}
must be incredibly fine-tuned.  The dressed coupling must be $\sim-(100\gev)^2$, but the bare coupling receives a positive quadratic correction $\sim\mplanck^2$. Let us calculate the evidence for the SM, given the electroweak scale and that the Higgs mass is $\sim125\gev$.  Whilst naively our Higgs potential is described by $\mu^2$ and $\lambda$, let us instead write the dressed dimension-two coupling as the sum of a bare coupling and a quadratic correction,
\begin{equation}
\mu^2 = \mu_0^2 + \Delta \mu^2,
 \end{equation}
and treat $\mu_0^2$, $\Delta \mu^2$ and $\lambda$ as separate parameters. \textit{A priori,} if the SM is coupled to the Planck scale, \mplanck, we expect that $\Delta \mu^2 \sim \mplanck^2$, and that $\lambda\sim1$, whereas we have no idea about the scale of $\mu_0^2$. Let us formalize these thoughts with logarithmic, scale invariant priors $\priorf{x}\propto 1/x$;
\begin{align}
\Delta \mu^2 &\text{ between } 10^{36} \text{ and } 10^{40} \gev^2,\\
\mu_0^2 &\text{ between } 10^{0} \text{ and } 10^{40} \gev^2,\\
\lambda &\text{ between } 10^{-3} \text{ and } 10^{1} .
\end{align}
We also note that \textit{a priori} $\mu_0^2$ could be positive or negative.

We calculate the evidence for the SM given the \mz measurement\cite{Beringer:1900zz} and the LHC $\mh\sim125\gev$ measurement\cite{Beringer:1900zz,Chatrchyan:2012ufa,Aad:2012tfa}. We approximate the likelihood functions for the measurements of \mz and \mh as Dirac delta functions;
\begin{align}
 \label{Eqn:SM_Evidence}
\ev_{\text{only }\mz} &= \frac {
\int \delta(\mz - 91.1876\gev)\frac{\text{d}\mu_0^2}{\mu_0^2}\frac{\text{d}\lambda}{\lambda}\frac{\text{d}\Delta\mu^2}{\Delta\mu^2}
}{
\int \frac{\text{d}\mu_0^2}{\mu_0^2}\frac{\text{d}\lambda}{\lambda}\frac{\text{d}\Delta\mu^2}{\Delta\mu^2}
},\\\nonumber
\ev_{\mh\text{ and }\mz}  &= \frac {
\int \delta(\mz - 91.1876\gev) \delta(\mh - 125.9\gev) \,\frac{\text{d}\mu_0^2}{\mu_0^2}\frac{\text{d}\lambda}{\lambda}\frac{\text{d}\Delta\mu^2}{\Delta\mu^2}
}{
\int \frac{\text{d}\mu_0^2}{\mu_0^2}\frac{\text{d}\lambda}{\lambda}\frac{\text{d}\Delta\mu^2}{\Delta\mu^2}
}.
\end{align}
The denominators normalize our logarithmic priors.
We integrate the Dirac delta functions with tree-level formulas for the Higgs and $Z$-boson masses (see \eg \refcite{Cheng:1985bj}),
\begin{align}
 \mh &= \sqrt{-2\mu^2},\\
 \mz &= g \sqrt{\frac{-\mu^2}{2\lambda}}.
\end{align}
We calculate evidences by performing the integrals in \refeq{Eqn:SM_Evidence} for two models:
\begin{enumerate}
\item The SM with quadratic divergences, $\Delta\mu^2\sim\mplanck^2$ , and
\item The SM without quadratic divergences,  $\Delta\mu^2=0$. \refcite{Bardeen:1995kv,Meissner:2006zh} argue that quadratic divergences vanish in theories with classical scale invariance without modifications to the $Z$-boson or Higgs boson masses.
\end{enumerate}

The resulting evidences are in  \reftable{Table:SM_CMSSM_evidences}. Unsurprisingly, the evidence for the SM with quadratic divergences is minuscule compared to that for the SM without quadratic divergences.
The Bayes-factors in \reftable{Table:SM_CMSSM_evidences} are more than $10^{30}$ against the SM with quadratic divergences ($150$ is considered \ic{very strong} on the Jeffreys' scale).

Let us interpret the evidence as a sampling distribution for the expected $Z$-boson mass, \ie plot the evidence as a function of \mz (\reffig{Fig:SM_Evidence}). As expected, the SM with quadratic divergences squanders its prediction for the $Z$-boson mass near \mplanck, far away from the measured \mz. The SM with quadratic divergences is unnatural. Because without quadratic divergences one can make no prediction for the magnitude of \mz, the SM without quadratic divergences is somewhat unnatural and complicated.

\begin{figure}[ht]
\centering
\includegraphics[height=0.49\linewidth]{Evidence.pdf}
\caption{The probability distribution of the $Z$-boson mass in the various models. The area under each plot is equal to one.}
\label{Fig:SM_Evidence}
\end{figure}

Now that we have completed the somewhat trivial exercise of calculating the evidences for the SM, let us calculate the evidences for the CMSSM.

\section{\label{Section:CMSSM_evidence}Bayesian evidences for the CMSSM}
The $Z$-boson mass, or, equivalently, the scale of electroweak symmetry breaking, is predicted in the MSSM via radiative electroweak symmetry breaking. At tree-level\cite{Martin:1997ns},
\begin{equation}
\label{Eqn:CMSSM_MZ}
\frac12 \mz^2 = - \mu^2 + \frac{\mhd^2 - \mhu^2 \tanbsq}{\tanbsq -1}.
 \end{equation}
This expression is problematic; it contains the \ic{little-hierarchy problem}\cite{Barbieri:2000gf} and the related \ic{$\mu$-problem}\cite{Kim:1983dt}. From experiments, we know that \mz is $\sim 100\gev$. The MSSM predicts \mz via a cancellation between the SUSY breaking parameters, $\mhu^2$ and $\mhd^2$, and a SUSY preserving parameter in the superpotential, $\mu$. If the SUSY breaking scale is greater than the measured value of \mz, a cancellation between such large numbers is somewhat miraculous. This is the little-hierarchy problem. This problem is statistical in nature; we are concerned that the MSSM is unlikely because its parameters must be fine-tuned, \ie it might only agree with experiments in a small fraction of its parameter space.

With a simplified \refeq{Eqn:CMSSM_MZ},
\begin{equation}
\frac12 \mz^2 \simeq -\mu^2 - \mhu^2,
 \end{equation}
we found an analytic expression for the evidence from \refeq{Eqn:Evidence} as a function of \mz in the CMSSM with logarithmic priors. We plot this expression as a function of the $Z$-boson mass in \reffig{Fig:SM_Evidence}. Whilst the CMSSM is somewhat fine-tuned, the fine-tuning of the SM with quadratic divergences is far worse. The SM dimension-two coupling is quadratically sensitive to the UV; the highest scales must enter our expression for \mz. In the SM with quadratic divergences, the cancellation resulting in \mz must involve quantities $\sim\mplanck$.  In the CMSSM, we require a cancellation, but the cancellation could be at any scale up to \mplanck.

Fine-tuning is typically measured with a sensitivity, for example, that originally proposed in \refcite{Ellis:1986yg,Barbieri:1987fn}, the Barbieri-Giudice measure,
\begin{equation}
\label{Eqn:BG}
\Delta_i = \frac{x_i}{\mz^2} \dpartial{\mz^2}{x_i},
 \end{equation}
where $x_i$ are the model's parameters. The reciprocal of this measure is, indeed, similar to our Bayesian evidence, in that a small Barbieri-Giudice measure indicates that the model might spend its probability mass around the measured value of \mz. This is illuminated by rewriting \refeq{Eqn:BG},
\begin{equation}
\Delta_i^{-1} = \[\frac{\Delta\mz^2}{\mz^2}\frac{x_i}{\Delta x_i}\]^{-1} \propto \frac{\Delta x_i}{x_i}.
 \end{equation}
The reciprocal of the Barbieri-Giudice measure is proportional to the local fraction of the model's parameter space in which \mz varies by $\Delta \mz$; in similarity, the evidence is a measure of the fraction of the model's prior volume in which the model agrees with experiments\cite{Cabrera:2008tj,Cabrera:2009dm,Fichet:2012sn}. The Barbieri-Giudice measure lacks, however, a formal interpretation and is, furthermore, a property of a point in the model's parameter space, rather than of the model itself (\latincf~the evidence).

Bayesian evidence automatically penalizes fine-tuning. Focusing mechanisms (\eg the focus-point\cite{Chan:1997bi,Feng:1999zg,Feng:2011aa}) are automatically incorporated. We must, however, choose \ic{honest} priors. In the CMSSM, we ought to formulate our prior beliefs in $\mu$ and $b$, the fundamental parameters, defined
\begin{align}
 W &\supset \mu H_u H_d,\\
 \mathcal{L}_{\text{Soft}} &\supset - b^2 H_u H_d + \text{c.c.}
\end{align}
For pragmatism, however, we exchange $\mu$ and $b$ for \mz and \tanb via \eg \refeq{Eqn:CMSSM_MZ}. We ought to transform our priors with the appropriate Jacobian, resulting in \textit{effective priors} for \mz and \tanb\cite{Allanach:2006jc,Allanach:2007qk,Cabrera:2008tj,Cabrera:2009dm,Cabrera:2012vu,Fichet:2012sn}.  With logarithmic priors for $\mu$ and $b$, our effective priors are
\begin{align}
\label{Eqn:BG_priors}
\priorf{\mz} &= \dpartial{\mu}{\mz} \priorf{\mu}  = \frac{2\mu}{\mz} \Delta_\mu^{-1} \priorf{\mu} = \text{const.}\,\Delta_\mu^{-1},\\
\priorf{\tanb} &= \dpartial{b}{\tanb} \priorf{b}  = \frac{\text{const.}}{b}\dpartial{b}{\tanb}.
\end{align}
The effective prior for \mz reveals the formal relationship between Bayesian statistics and the Barbieri-Giudice measure\cite{Cabrera:2008tj,Cabrera:2009dm}. With the Barbieri-Giudice measure, the statistical nature of the problem is latent\cite{Giusti:1998gz,Strumia:1999fr}; it is now manifest.

We calculated the evidence exactly in the CMSSM with \ic{honest} priors. Let us make our prior choices clear, because it is a potential source of confusion. For the fundamental CMSSM parameters and priors we pick\footnote{One might wonder whether we should pick, \eg  $\mzero^2$ rather than \mzero as a fundamental parameter, since it is the square which appears in the soft-breaking Lagrangian. Because we pick logarithmic priors, however, the choice is irrelevant.}
\begin{align}
\mzero &\text{ log prior between } 1\gev \text{ and } \mplanck,\\\nonumber
\mhalf/\mzero &\text{ log prior between } 10^{-3} \text{ and } 10^3,\\\nonumber
\azero/\mzero &\text{ linear prior between } {-5} \text{ and } 5,\\\nonumber
b/\mzero &\text{ log prior between } 10^{-3} \text{ and } 10^3,\\\nonumber
\mu &\text{ log prior between } 1\gev \text{ and } \mplanck.
\end{align}
We anticipate that a breaking mechanism might distribute the SUSY breaking masses about a common SUSY breaking scale\cite{Cabrera:2009dm}, which we pick as \mzero. We do not consider mechanisms in which SUSY breaking parameters are split into distinct groups separated by many orders of magnitude\cite{Giudice:2004tc,ArkaniHamed:2004fb}. We call this choice of priors and parameterization our \textit{de jure} priors.

Were we to numerically calculate the evidence for the CMSSM with our \textit{de jure} priors, we would waste CPU time considering parameter space with incorrect \mz. For the purpose of our numerical calculation, we transform our \textit{de jure} priors into our equivalent \textit{de facto} priors,
\begin{align}
\mzero &\text{ log prior between } 1\gev \text{ and } 20\tev,\\\nonumber
\mhalf/\mzero &\text{ log prior between } 10^{-3} \text{ and } 10^3,\\\nonumber
\azero/\mzero &\text{ linear prior between } -5 \text{ and } 5,\\\nonumber
\tanb &\text{ effective prior between } 1 \text{ and } 60,\\\nonumber
\mz &\text{ effective prior, fixed } 91.1876\gev,
\end{align}
where the effective priors are in \refeq{Eqn:BG_priors}. The \ic{missing} parameter space in our \textit{de facto} priors at $\msusy\gg20\tev$ is irrelevant in our calculation, because it contains negligible evidence. The \ic{missing} parameter space, however, results in differences in normalization between our \textit{de jure} and \textit{de facto} priors, which we correct by hand. Fortunately, because the sign of $\mu$ is identical at the electroweak and \mplanck scales, we require no Jacobian to transform our prior for \sgnmu from \mplanck to the electroweak scale.

We pick informative, Gaussian priors for the SM nuisance parameters \mts, \mbs, \invalphas and \alphass\cite{Beringer:1900zz}. \refcite{Cabrera:2008tj} stresses that the top and bottom masses are derived parameters; the input parameters are the Yukawa couplings, $y_t$ and $y_b$. In the CMSSM, the relationship between fermion masses and the Yukawa couplings includes factors of $\sin\beta$ and $\cos\beta$. We should pick priors for the Yukawa couplings rather than for the fermion masses; however, at leading order with logarithmic priors for the Yukawa couplings, there is no effective prior associated with $(y_t, y_b)\to(\mts,\mbs)$. At leading order, our treatment of the SM nuisance parameters is equivalent to picking logarithmic priors for the Yukawa couplings.

We calculated the CMSSM's mass spectrum and effective priors with \texttt{SOFTSUSY}\cite{Allanach:2001kg}. We used \texttt{MultiNest}\cite{Feroz:2008xx} with \texttt{PyMultiNest}\cite{Buchner:2014nha} to perform the integral in \refeq{Eqn:Evidence}. We found the evidence for three cases:
\begin{enumerate}
 \item $\mz=91.1876\gev$\cite{Beringer:1900zz} only in our likelihood (fixed by our \textit{de facto} priors),
 \item \mz, $\mh=125.9\pm0.4\pm2.0\gev$\cite{Beringer:1900zz,Chatrchyan:2012ufa,Aad:2012tfa,Allanach:2004rh} and the null result from the LHC in $20\invfb$\cite{ATLAS-CONF-2013-047} in our likelihood, and
 \item \mz, \mh and a hypothetical null result from the VLHC in $3000\invfb$\cite{Cohen:2013xda} in our likelihood.
\end{enumerate}
In the first case, our likelihood for \mz is a Dirac delta function. In the second case, our likelihood for \mh is a Gaussian with theoretical and experimental errors added in quadrature, and we veto points that are excluded by an ATLAS search for jets and missing energy\cite{ATLAS-CONF-2013-047}. In the last case, we consider the potential consequences of the \roots{100} VLHC, by vetoing points that would be excluded by a null result in $3000\invfb$\cite{Cohen:2013xda}, \ie points with $\ms{g}\lesssim16\tev$ and $\ms{q}\lesssim16\tev$ or points with $\ms{g}\lesssim13.5\tev$.

\begin{table}[ht]
\begin{center}
\begin{ruledtabular}
\begin{tabular}{llll}
& \mz &  \mz, \mh and LHC & \mz, \mh and VLHC\\
 \hline
 Evidences, \ev & $\text{GeV}^{-1}$ & $\text{GeV}^{-2}$ & $\text{GeV}^{-2}$\\
\hline
SM with quadratic divergences & \pn{9}{-37} &  \pn{2}{-40}   & \pn{2}{-40}\\
SM no quadratic divergences                            & \pn{1}{-4}    &  \pn{2}{-7}     & \pn{2}{-7}\\
CMSSM						             & \pn{8}{-5}    &  \pn{3}{-10}  & \pn{7}{-13}\\
\hline
 Bayes-factors, $B=\ev_a/\ev_b$\\
\hline
CMSSM/SM with quadratic divergences      & \pn{9}{31} & \pn{2}{30} & \pn{4}{27}\\
SM no quadratic divergences/CMSSM                                & \pn{2}{0}    & \pn{7}{2} & \pn{3}{5}\\
\hline
 Partial Bayes-factors, $P=B_{i+1}/B_i$\\
\hline
SM no quadratic divergences/CMSSM & $\sim2$ & $\sim500$ & $\sim400$\\
\end{tabular}
\end{ruledtabular}
\end{center}
\caption{Bayesian evidences  and Bayes-factors for the SM with quadratic divergences, SM without quadratic divergences and CMSSM. The headings indicate which experimental results were included. The final column is the \ic{fine-tuning price,} as measured by partial Bayes-factors.}
\label{Table:SM_CMSSM_evidences}
\end{table}

The evidences for the CMSSM in our three cases are shown are shown in \reftable{Table:SM_CMSSM_evidences}. Let us discuss the results case by case:
\begin{enumerate}
  \item\textit{\mz only in our likelihood.} The Bayes-factor favors the CMSSM over the SM with quadratic divergences by $\sim10^{32}$; as anticipated, the CMSSM is favored by naturalness. The Bayes-factor favors the SM without quadratic divergences over the CMSSM by only $\sim2$, which is \ic{barely worth mentioning} on the Jeffreys' scale in \reftable{Table:Jefferys_Scale}. Prior to LHC experiments, the CMSSM was not unnatural.

  \item\textit{\mz, \mh and LHC $20\invfb$ in our likelihood.} The Bayes-factor favors the CMSSM over the SM with quadratic divergences by $\sim10^{30}$; the little-hierarchy problem in the CMSSM is minuscule compared with the hierarchy problem in the SM with quadratic divergences.  The Bayes-factor favors the SM without quadratic divergences over the CMSSM by $\sim700$ ($150$ is \ic{very strong} on the Jeffreys' scale). Relative to the SM without quadratic divergences, the evidence for the CMSSM diminishes by a factor of $\sim500$; this is the \ic{fine-tuning price} of the LHC.

  \item\textit{\mz, \mh and a hypothetical null result from VLHC $3000\invfb$ in our likelihood.} The Bayes-factor favors the SM without quadratic divergences over the CMSSM by $\sim10^5$. Relative to the SM without quadratic divergences, the evidence for the CMSSM diminishes by a further factor of $\sim400$. The \ic{fine-tuning price} of null results from the VLHC ($\sim400$) would be similar to, though slightly less than that of the LHC ($\sim500$).

\end{enumerate}
Note that in all cases, however, the Bayes-factors favor the CMSSM over the SM with quadratic divergences by $\gtrsim10^{27}$. The \ic{fine-tuning prices} for the experiments in the CMSSM are illustrated in \reffig{Fig:FT_price} by the logarithm of the Bayes-factor for the SM without quadratic divergences against the CMSSM.

\begin{figure}[ht]
\centering
\includegraphics[height=0.49\linewidth]{Price.pdf}
\caption{The \ic{fine-tuning prices} of the \mz measurement, LHC experiments and hypothetical null results from the VLHC.  Our \ic{fine-tuning prices} are the Bayes-factors for the SM without quadratic divergences against the CMSSM broken down by experiment. \mz indicates the measurement of the $Z$-boson mass, \mh and LHC indicates the LHC Higgs mass measurement and null results from LHC, and VLHC indicates hypothetical  null results in $3000\invfb$ at \roots{100}. The logarithm is base 10.}
\label{Fig:FT_price}
\end{figure}

The posterior probability density (see \eg \refcite{Fowlie:2011mb} for an introduction) is a by-product of the \texttt{MultiNest} evidence calculation.
With \mz, \mh and null results from the LHC in our likelihood, the posterior probability density  for \pmm confirms that the focus-point\cite{Chan:1997bi,Feng:1999zg,Feng:2011aa} at $\mzero\sim8\tev$ and $\mhalf\lesssim2\tev$ is favored. With only \mz in our likelihood,  unsurprisingly, we find that $\msusy\sim\mz$ is favored by \mz, \ie by naturalness.

\section{\label{Section:Fair_Comp}Effective versus UV-complete theories}
We interpreted the SM as an effective theory valid only below a cut-off scale, $\Lambda=\mplanck$, above which, we presumed, quantum field theory (QFT) is significantly modified by new physics (NP) related to gravity. The SM has a single relevant operator, $\mu^2\phi^2$. We considered the finite bare mass, $\mu^2$, to be a physical parameter originating from NP.  We parameterized our ignorance of $\mu^2$ with a logarithmic prior. We assumed that quadratic corrections to $\mu^2$ are unaffected by NP below the Planck scale, hinted at by \eg neutrino masses, inflation and dark matter.

If one attempts to remove the cut-off from the SM, $\Lambda\to\infty$, the bare mass diverges. The renormalized mass, $\mu_\text{R}^2$, might be considered to be fundamental. The renormalized mass differs from the bare mass by a quadratic correction and scheme-dependent terms. Because there are no quadratic corrections to the renormalized mass, it runs logarithmically from the Planck scale to the EW scale. The hierarchy problem is hidden in counter-terms. There are numerous problems with such an approach, \eg triviality.

We, however, interpreted the CMSSM as an ultra-violet (UV) complete theory valid at all scales. The fundamental parameters were renormalized SUSY breaking masses defined at the renormalization scale $\mu=\mgut$ in the minimal subtraction scheme, rather than bare masses. Was it fair to compare the SM as an effective theory with the CMSSM as a UV-complete theory? Whilst with Bayesian evidence one can compare any models that make predictions for the experimental data, comparisons are interesting only if the models are realistically interpreted.

Suppose we instead interpreted the CMSSM as an effective theory valid only below a cut-off scale, $\Lambda=\mgut$, at which new GUT physics is important, or the Planck scale, at which gravitational interactions mediate SUSY breaking.  Divergences are no worse than logarithmic in supersymmetric models. The bare SUSY breaking masses at the cut-off scale would be similar to renormalized SUSY breaking masses at the GUT or Planck scales; the bare and renormalized masses would differ by logarithmic corrections. No fine-tuning of the EW scale is hidden by parameterizing the CMSSM as a UV-complete theory with renormalized masses.\footnote{It is possible, however, that focusing mechanisms are disfavored if SUSY breaking masses are unified at the Planck scale rather than at the GUT scale\cite{Polonsky:1994sr}.}

The comparison was fair, though its outcome was perhaps inevitable. Although the SM was vastly disfavored, it was important in the analysis; it was a reference model against which we judged the severity of the change in the \ic{fine-tuning price} in the CMSSM.

\section{\label{Section:Mu_problem}The \texorpdfstring{$\mu$}{mu}-problem}
A problem emerges from our \ic{honest} choice of prior for $\mu$, which aggravates the fine-tuning problem. The $\mu$-parameter is a symmetry conserving parameter in the superpotential. \textit{A priori,} it is unrelated to a symmetry breaking scale. This is problematic; phenomenologically it must be that $\mu \sim \msusy$.
The evidence for a model in which we expect $100\gev\lesssim\mu\lesssim\mplanck$ and observe $\mu \sim \msusy$ could be  smaller than that for a model in which we expect $\mu \sim \msusy$ and observe $\mu \sim \msusy$.
This is the \ic{$\mu$-problem;} in our formulation, its statistical nature is manifest.  \refeq{Eqn:BG_priors} reveals the $\mu$-problem and the fine-tuning problem;  the $\mu$-problem is that \priorf{\mu\approx\msusy} is small and the fine-tuning problem is that \indpartial{\mu}{\mz} is small, resulting in a small prior belief in the observed electroweak scale, \priorf{\mz}. The ratio of evidences for a model that predicts $\mz\lesssim\mu\lesssim\mplanck$ and an \ic{almost-so} model that predicts \eg $10^{-1}\msusy\lesssim\mu\lesssim10^3\msusy$ is approximately
\begin{equation}
\frac{\ln\(\frac{10^{3}\msusy}{10^{-1}\msusy}\)}{\ln\(\frac{\mplanck}{\mz}\)}\approx \frac15.
 \end{equation}
A similar result applies to SUSY models with a Giudice-Masiero mechanism\cite{Giudice:1988yz}.

The little-hierarchy problem in the CMSSM is $\sim30$ times worse than the $\mu$-problem. The $\mu$-problem contributes a factor of only $\sim5$ to a Bayes-factor for an \ic{almost-so} model against the CMSSM. The Bayes-factor with \mz, \mh and null results from the LHC favors the SM without quadratic divergences over the CMSSM by $\sim700$; the little-hierarchy problem contributes a factor of  $\sim150$ and the $\mu$-problem contributes a factor of $\sim5$.

\section{\label{Section:Conclusions}Conclusions}
The absence of SUSY or other new physics at the LHC has lead many to question naturalness arguments. Drawing upon the literature, we clarified the relationship between Bayesian statistics and naturalness, concluding that natural models are most probable and that naturalness is not merely an aesthetic principle. We calculated the Bayesian, probabilistic measure of naturalness, the evidence, for the SM with and without quadratic divergences, demonstrating that the SM with quadratic divergences is improbable. We calculated the evidence for the CMSSM in three cases: with only the \mz measurement; with the \mz measurement and LHC measurements; and with the \mz measurement and a hypothetical null result from the VLHC with $3000\invfb$.
The latter allowed us to quantitatively understand the potential \ic{fine-tuning price} of the VLHC. We found that the \ic{fine-tuning price} of null results from the VLHC ($\sim400$) would be slightly less than that of the LHC  ($\sim500$).  We hope this result might help to inform preliminary discussions and plans for the VLHC.

\begin{acknowledgements}
I thank M.~Raidal and A.~Strumia for helpful criticisms of my manuscript.
This work was supported in part by grants IUT23-6, CERN+,  and by the European Union
through the European Regional Development Fund and by ERDF project 3.2.0304.11-0313
Estonian Scientific Computing Infrastructure (ETAIS).
\end{acknowledgements}

\let\cite\oldcite

redpajama_set_name: RedPajamaArXiv


text:
Salary of an Amateur Comedian
Leonard Dozier
Occupational ... $44,255/year /> 2012-2016 +12.0%
Plumbers, ... $52,590/year /> 2012-2016 +13.1%
Becoming the next Eddie Murphy, Jerry Seinfeld or Kevin Hart is the dream of an amateur comedian. Reaching this dream comes with the sacrifice of "paying dues," which means working for little to no money and performing as often as possible to gain exposure. While amateur comedians may occasionally earn money for performing, the general rule is that they earn very little annually and mostly work for free.
Amateur comedians may start out working for parties then gradually finding gigs at clubs and restaurants if they are fortunate. The goal of any amateur comedian is to find professional work. As such, building a resume is most important in trying to find jobs that pay a salary.
The Open Mic Scene
Performing at open mics is how most amateur comedians fine-tune their technique. Comedy at open mics can be found in cities across the country, such as New York City and Los Angeles, and smaller cities such as Seattle and Dallas. Some venues may charge a small fee for patrons, such as Mr. Spot's Chai House in Seattle, which sometimes charges $5 for shows, according to a December 2007 "Seattle Times" article. The small dollar amount and relatively small crowds at such venues ensure very meager earnings for comedians.
Amateur comedians often perform at night and earn their salaries from day jobs. The December 2007 "Seattle Times" article provided an example in Danielle Radford, an amateur comedian who worked as a court secretary during the day. She admitted to performing at four to five open mics per day. Similarly, Dan Carroll, an amateur comic and owner of People's Republic in Seattle, was a radio advertising salesman.
Salary Opportunities
Through shows such as "Last Comic Standing," amateur comedians can reach millions of viewers, earning fame and instant fortune. The show's Season Three winner, Alonzo Bodden, has appeared on the "Tonight Show with Jay Leno," "Late Night with Conan O'Brien" and in movies such as "Scary Movie 4." In prominent open-mic clubs such as The Big Deuce on Madison Avenue in New York City, amateur comedians compete for opportunities to be booked for feature shows at the club in addition to the opportunity to emcee a show.
How Much Is a Professional Singer Paid Per Month?→
Salary of a Ringmaster→
How to Become an Entertainer→
The Average Salary of a Professional Figure Skater→
Broadway Actress Salary Per Year→
What Do Ballerinas Earn?→
The Seattle Times; Seattle's Amateur Stand Up Comedy...; Haley Edwards; December 2007
Reality World; Former Last Comic Standing Winners...; Christopher Roccio; June 2007
The Badger Herald; Big Deuce Gets Big Laughs; Stephen Dixon; January 2011
Leonard Dozier is a freelance writer based in southern New Jersey and New York. His film and sports columns have been published by "Casino Connection Magazine" and Trev Rogers sports respectively. A prolific and extremely versatile writer, he is an ASCAP songwriter and has written screenplays and stage plays registered with the Writer's Guild of America.

redpajama_set_name: RedPajamaCommonCrawl


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It all started on 5th January 2006. I remember hearing about the competition in the previous series, but on this night I wanted to see Kenny Mayne, a studio personality on ESPN. My wife knew even less, but she also watched. Now it was done.
After dinner the next day, we gathered in our family room. My wife asked, "Don't you want to see how it ends?" I wasn't interested because I saw what I wanted – but she wanted to watch – so we did. Since then, we can use our fingers to count the number of episodes we've missed of Dancing with the Stars (DWTS).
DWTS became a topic within our dinner group as several others watched the show. In 2007 (I think), the hosting couple hired a local dance instructor to give the dinner group couples an introductory lesson. A few months later, I purchased an introductory package of ballroom dance lessons for my wife as a birthday gift.
That initial purchase led to other lessons … and then more lessons. Who knows how much money we've spent on dance since then, but we are still dancing and having fun!
No – we don't have any glamorous outfits – those are very expensive – but outside of lessons, we've purchased shoes and attended numerous dance events. We don't compete or perform – so no need for the glamour and glitz. We dance for fun!
Ballroom dance is a journey and more difficult than one may think. I recall our first lessons were in April, so we were excited to dance at an upcoming wedding 6-8 weeks later. We struggled at the wedding, so the lesson learned was to continue maximizing the Safe House for Dance – the studio where we take lessons.
We continued our weekly pattern of a private lesson, a group lesson, and the Friday night socials. That's how you learn to dance. That's how you increase your confidence. By the end of summer, we were ready to again try dancing in public.
That was our breakthrough dance. We felt confident enough not to worry, wise enough to know we still had a lot to learn, and comfortable enough to laugh after mistakes – stop and start again. A month later we were on a cruise ship where we danced every day – and had dance friends on ships. The rest is history.
We still take private lessons – still attend groups – still dance socially most weeks – we still don't compete or perform – and we still enjoy it. We have a circle of dance friends, and know many other because of ballroom. Life is good.
On his blog, A Frank Angle: Thoughts from the Inner Mind, Frank shares his honest views on a variety of topics. He frequently writes about ballroom dance – in posts such as Benefits of Ballroom Dance, A Dance with Grace and Elegance, My Close Encounter, A dance story: My attempt at fiction, An unexpected place ballroom dance led me, and Tango.
And Craig and I have seen you dance beautifully!
That a way … how long have you been dancing ballroom?
Frank, I've never danced ballroom – just flamenco & Argentine tango. My husband & I took flamenco lessons for about 5 yrs. Then moved onto partner dancing of Arg. tango about 6 yrs. ago. Ballroom dancers have to learn so many dances!
That's interesting … this summer we took 4 group classes for beginners of Argentine Tango … interesting … may be learning more this fall. The feel for ballroom was helpful. Still dancing?
Hope you enjoy it. Yes – in fact, we spent tonight practicing sevillanas & tango for a dear friend's upcoming birthday celebration.
Thank you, Frank – I hope everyone visits your wonderful blog!
Yes, practice makes perfect … or … just lots of FUN !!!
in the very addictive world of dance. Well done, aFrank.
Isadora … Hey hey hey!!!! Many thanks for popping over for this post … and I knew you would enjoy it. Everything has a history, so thought I'd share mine regarding this hobby.
Indeed it does, Bun. I love it also for fact that it offers a non-verbal way to interact with my beloved husband. Words can be clumsier than dance steps.
That's a sweet sentiment! I'm afraid in my case, though, my dance steps are almost certainly clumsier.
The circle of friends has been one of the biggest surprises in our journey. In the last paragraph, there is a link to a post I did featuring benefits of ballroom dance. Thanks for reading.
That's great. Thanks for the link, by the way.