MonDie

CharonY, could it boil down to whether variable survival is more due to internal variables or external variables? For example, if all individuals experience the same external influences, then variable survival can only be due to internal differences.

This is not a unique problem. https://en.wikipedia.org/wiki/Type_I_and_type_II_errors

Where would that fall into the Hardy-Weinberg principle if not "selection"? Returning Dec 19

BigNose, I think you're overestimating me. I had a natural talent for math, but I've mostly studied science. I'm going to take a month-long break from this forum, but I'll still see any follow-up posts. I can understand the reasoning behind most of that equation, although I don't see why a sample's variance is being weighted relative to its mean, nor why they never "unsquare" for the squaring occurring inside te brackets, nor why their calculated combined variance was the same as the variance of the male sample. And I think it thus follows that I have no idea how it relates to Bessel's correction. Nevertheless, I understood studiot's calculations, and I saw for myself that Bessel's correction works at least for multiple variances from multiple samples.

Overtone, I see what you're saying. All participants were conscious. A direct test of the role of consciousness would have included an un-conscious control group, which would require a completely objective measurement that doesn't rely on verbal reports. I have not read, nor do I think I'll have the background to appreciate, the rest of Wolpe and Rowe. However, they note some limitations of the classical Libet design, which determines the timing of mental events via the reports of participants staring at a "clock". They had explained how Libet's methods improved upon the previous "subjective" measures. "For example, such experimental paradigms involve asking subjects to rate how much they felt in control of a certain movement, or whether a sensory stimulus was felt to be the result of their own action (Wolpe & Rowe, 2013)." I think they're saying that even Libet's methods are subjective because they still rely on a verbal report. The paper then goes on to discuss more objective measures of agency... (to be continued?)

Please use quotes to delineate where the circularity occurred, or else don't insert circularity where there is none. "Fitness" is that which is naturally selected. "Natural selection" is (net) change in allele frequencies due to the interaction between individuals and their (natural) environment. "Allele frequency" is a quantification of how often that allele occupies that locus in that population. "Population" is a group of interbreeding individuals. I can go on, but I cannot go on infinitely. I don't see the difference between a stochastic event and a narrowly defined event. Most individual events are random, but the sum of their effects is nonrandom.

I should acknowledge in this thread that I probably misrepresented and oversimplified the calculations. My separate thread concerning only the maths may be merged at your discretion. http://www.scienceforums.net/topic/86520-twin-data-math-clarify-rijsdijk-sham/ However, Rijsdijk & Sham, 2002 say that twin methods rest on several assumptions which pertain to the discussion here. http://bib.oxfordjournals.org/content/3/2/119.long I also want to note that MZ and DZ twins would also share cohort effects, which would make cohort effects shared-environment too. Fru ̈hling V. Rijsdijk and Pak C. Sham. Analytic approaches to twin data using structural equation models. Briefings in Bioinformatics. 2002 Jun;3(2):119-33.

I didn't represent it correctly, but I had the correct meaning in my head. BigNose, I didn't know any calculus until your integrals spurred some independent learning. Now I understand that the integral of an interval of a function is the mean y-value multipled by the interval (I avoided the words "area" and "volume" because my understanding is that the lower quandrants are scored negatively). I also read the first six lessons of Capn's derivatives tutorial. Although your equation may not be true, I do want to know how you were using integrals to represent variance. I'm not familiar with integral notation yet.

More fitting for a veganism thread. http://www.academicjournals.org/article/article1379770665_Banjo%20et%20al.pdf

Not quite the subject of the thread. Like most TED talks, that was very very simplistic. The guys who are giving up porn obviously aren't the same as an randomly assigned experimental group, so researchers will either do a regression analysis or control for potential confounds. He seems to have forgotten to mention that. And of course a quitting addict is going from one extreme to another without ever crossing the middleground. In contrast, it's also argued that porn availability reduced the rape rate. I guess rape isn't unhealthy for the perpetrator... Internet addiction sounds intrigueing. Coincidentally, I won't be back back until December 19th... but I get 5 mercy posts.

I would ingest the raw whole grains for their precious fiber and chitin.

This thread was a question about the plausibility of free won't until I discovered the countering research. Now that I'm in over my head anyway, I found what I can already tell is going to be a good review of the research. Beyond the "urge to move": objective measures for the study of agency in the post-Libet era. (Wolpe & Rowe, 2014) http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4064703/pdf/fnhum-08-00450.pdf

Regarding the link, I don't understand why they're summing the sample variance [math]S_{i}^{2}[/math] with the difference between means squared [math](\bar{x}_{i} - \bar{X})^{2}[/math]

You didn't include overbar! \bar{x} [math]\bar{x}[/math] But this was the first thing I couldn't find in the tutorial. nice job

A trait is usually some manifestation of an allele. For example, purple petals and white petals are two possible traits for pea plant petal color, and they result from different alleles at the same locus.

I've never seen food advertised as non-processed in the USA. Saying that processed is unhealthy may be a blanket statement, but blankets are useful in the absence of case-specific details.

BigNose, in the link, are those brackets for absolute value or a floor/ceiling function?

Time travel is impossible. The pre-modern scientist would have been justified given his time period.

The null hypothesis is that survival is random. Then allele frequencies wouldn't change in a sufficiently large population. Natural selection explains why allele frequencies do change in sufficiently large populations, and why different changes occur in isolated populations. Prepare separate cultures or populations. Expose the experimental group to the mechanism of selection.

It was clearly defined there in the original Greek!

I made a mistake. I was using [math]\mu[/math] where I should have used [math]\sigma^{2}[/math], but I was still talking about variance. If you take the average value of [math]|s_{i}-\sigma|[/math] or [math]|s_{i}^{2}-\sigma^{2}|[/math], you find that Bessel's correction results in a higher average error (with the numbers given).

I think Matt wants the word "physical" in place of "scientific". http://plato.stanford.edu/entries/physicalism/

Yes, I see that now (although you seem to define "variance" differently). That last post was an edit rollercoaster because I was confusing the population n with the sample n. From studiot's numbers, however, it looks like n-0 is preferable if I want to estimate the population mean from only one sample.

I stopped using my food processor after learning of the unhealthfulness of processed foods.

Studiot, I understand your attachment. Calculating the sample variances (s) with n-1 leads to an average s that equals the population variance ([math]\mu[/math]). However, giving an average that is closer does not mean it's a better estimate. Using those same numbers... If you calculate the average absolute deviation of s from [math]\mu[/math], i.e. the value of [math]\frac{\sum|s_{i} - \mu|}{n}[/math], or even if you find the square root of the average of the error squared, [math](\frac{\sum(s_{i} - \mu)^{2}}{n})^{0.5}[/math], you find that n-1 produces more error (59.44, 74.5) than n-0 (48.3, 50.22).