I will continue with the subject today. As it was pointed out previously in part II:
We are faced with how to use science daily
and how to apply it to triathlon; the problem of how to refine our daily
thinking. We have plenty of information
to draw from using Aristotle´s logic for that purpose. There is a book written by Alan Kazdin that
explains in details how to THINK SCIENTIFICALLY.
Kazdin’s approximately 700
publications include 48 books that focus on interventions for children and
adolescents, cognitive-behavioral treatment, parenting and child rearing,
interpersonal violence, and methodology and research design.[5]His work on parenting and
child rearing has been featured on CNN,[6] NPR,[7] PBS,[8] BBC,[9] and he has appeared on
Good Morning America,[10] ABC News,[11] 20/20, The Dr. Phil Show,[12] and the Today Show.[13]
We outlined several points already but I would mention
others:
1) How we choose our groups to study. Regarding this matter we should consider the
multiple variables in a group: age, time of starting training, accumulated
hours of training, quality of training including technique (they can do hours
of low heart rate training but without the proper technique is like practicing
another sport). Kazdin points out the difficulties regarding this kind of
research that uses statistics and conducts to different policies. I
photocopied a page from his book. He wrote a great book.
2)
What kind of
obstacles the athletes are going through?
In Mexico, the education to success is the big player. Even people with a high
socio-economical-status cannot be successful because their education is not
according to a high socio-economic status for the first world´s education to be
successful in a regular job; and being a professional triathlete should be
considered a regular job for special people.
THIS POINT IN ITSELF IS A VARIABLE THAT CHANGES EASILY THE OUTCOME OF
THE TRIATHLETE. After 20 years of
empirical research this variable is what has stopped us from winning a World
Championship in triathlon. It is not a
matter of “taking a bad decision.” The
culture in which our athletes are in does not allow taking different decisions;
impulsivity, immediate gratification, lack of perseverance and attention to
details are part of the cultural problems they have to overcome. Octavio Paz, Nobel Prize Winner said, and
adding to what it is mentioned:
Después de siglos de fracasos, en lo único que creemos los mexicanos es
en la Virgen de Guadalupe y la lotería nacional. (After so many years of failures, the only
things left to believe in are: the Virgin of Guadalupe and the lottery).
There are many variables that are impossible to
measure and that is why Einstein says that when reality is very complex science
fails. Human beings have many variables
and that is why a one subject study is
the way to go. We do not have a great
number of athletes to have a good statistic value to generalize findings. We started the series Physiology for Dummies
based on this premise. 11 mars 2012
3) I will give some useful
information regarding the sample size needed in case you really want to know
about research:
Sample Size Calculator
Terms: Confidence Interval & Confidence Level
The confidence interval (also called margin of error) is the
plus-or-minus figure usually reported in newspaper or television opinion poll
results. For example, if you use a confidence interval of 4 and 47% percent of
your sample picks an answer you can be "sure" that if you had asked
the question of the entire relevant population between 43% (47-4) and 51%
(47+4) would have picked that answer.
The confidence level tells you how sure you can be. It is
expressed as a percentage and represents how often the true percentage of the
population who would pick an answer lies within the confidence interval. The
95% confidence level means you can be 95% certain; the 99% confidence level
means you can be 99% certain. Most researchers use the 95% confidence level.
When you put the confidence level and the confidence interval together,
you can say that you are 95% sure that the true percentage of the population is
between 43% and 51%. The wider the confidence interval you are willing to
accept, the more certain you can be that the whole population answers would be
within that range.
For example, if you asked a sample of 1000 people in a city which brand
of cola they preferred, and 60% said Brand A, you can be very certain that
between 40 and 80% of all the people in the city actually do prefer that brand,
but you cannot be so sure that between 59 and 61% of the people in the city
prefer the brand.
Factors
that Affect Confidence Intervals
There are three factors that determine the size of the confidence
interval for a given confidence level:
- Sample size
- Percentage
- Population size
Sample Size
The larger your sample size, the more sure you can be that their answers
truly reflect the population. This indicates that for a given confidence level,
the larger your sample size, the smaller your confidence interval. However, the
relationship is not linear (i.e., doubling the sample size does not halve the
confidence interval).
Percentage
Your accuracy also depends on the percentage of your sample that picks a
particular answer. If 99% of your sample said "Yes" and 1% said
"No," the chances of error are remote, irrespective of sample size.
However, if the percentages are 51% and 49% the chances of error are much
greater. It is easier to be sure of extreme answers than of middle-of-the-road
ones.
When determining the sample size needed for a given level of accuracy
you must use the worst case percentage (50%). You should also use this
percentage if you want to determine a general level of accuracy for a sample
you already have. To determine the confidence interval for a specific answer
your sample has given, you can use the percentage picking that answer and get a
smaller interval.
Population Size
How many people are there in the group your sample represents? This may
be the number of people in a city you are studying, the number of people who
buy new cars, etc. Often you may not know the exact population size. This is
not a problem. The mathematics of probability proves the size of the population
is irrelevant unless the size of the sample exceeds a few percent of the total
population you are examining. This means that a sample of 500 people is equally
useful in examining the opinions of a state of 15,000,000 as it would a city of
100,000. For this reason, The Survey System ignores the population size when it
is "large" or unknown. Population size is only likely to be a factor
when you work with a relatively small and known group of people (e.g., the
members of an association).
The confidence interval calculations assume you have a genuine random
sample of the relevant population. If your sample is not truly random, you
cannot rely on the intervals. Non-random samples usually result from some flaw
in the sampling procedure. An example of such a flaw is to only call people
during the day and miss almost everyone who works. For most purposes, the
non-working population cannot be assumed to accurately represent the entire (working
and non-working) population.
http://www.surveysystem.com/sscalc.htm
