Official 2014 US 10 Mile Champs Thread - Bawcom sets AR!! Christo Landry dominates in 46:41!

Place Name Bib Age Place Gender Place 5 Mile Split 10k Split Clock Time Net Time Pace Hometown

WOMEN -- 40 THROUGH 44

1 BRENDA HODGE 241 42 87 19 21:52 30:09 55:54 55:54 5:35.3 YORK, PA

Obliterates American Womens Masters 5m and 10k records enroute, can't quite hang on for 10m record.

Deena Kaster 55:14.

oooooops wrote:

Place Name Bib Age Place Gender Place 5 Mile Split 10k Split Clock Time Net Time Pace Hometown

WOMEN -- 40 THROUGH 44

1 BRENDA HODGE 241 42 87 19 21:52 30:09 55:54 55:54 5:35.3 YORK, PA

Obliterates American Womens Masters 5m and 10k records enroute, can't quite hang on for 10m record.

statty guy wrote:

No use arguing with this guy he obviously doesn't understand statistics nor population samples....

You wanna try me? I've taught Statistics. I've also done my own stats using the IAAF performance lists over several years. You can take the IAAF Scoring tables to figure out time conversions between 10 mi. and HM (within a single gender), and then use the Mercier Calculator to compare genders (the only valid method for comparing genders, unless you want to do the stats yourself with the IAAF data), or rank order times to compare yourself.

Men like to argue that "women lack depth" and "lack competitiveness". This is no longer a valid argument to support this stance, as women's depth up to the half marathon exceeds men's. You can look at the IAAF data over several years, even as women have surpassed men in depth, and you still couldn't make a linear comparison (nor is the gap closing/becoming more linear). Women's times are positively skewed to the right and don't follow a normal distribution-- hence, why you can't make a linear comparison of "x minutes off the WR" or "% off the World Record" to compare genders. You have to use a non-parametric method of comparison, such as rank ordering times.

Glad I wasn't taught by you. Normal distribution is valid in a normal population. You have not taken that into account. You do measure them in a linear manner because there sample data is objective in nature. Curious as to where you taught? At a school that rarely meets test standards? You still haven't said which race you were referring to where 10 mile winners are Americans and breaking 48 with ease? Waiting

I'm not saying they lack competitiveness but it's quite objective add clear when testing population spacing from outlier that elite women typically run much slower than top .05 whereas men have less disparity in time from top .05 compared to the next 10 percent. Women have a very large disparity if you compare top .1 to next top .1 i.e. 20th percentile. That is a clear conclusive test of competitiveness at the elite level.

You did not teach stats, trust me. You lack simple knowledge. Anyone can lie and state false stories like that they worked with data for iaaf and such, but your lack of knowledge proves otherwise.

comparing standard deviations is quite simple and easy test to measure competitiveness of any race, you should learn how to use it! I can give lessons on here if you'd like

Glad I wasn't taught by you! The women's population violates the first assumption- following a normal distribution, which again is why you can't make a linear comparison and have to use a non-parametric measure such as rank-ordering.

So you're confirming what I stated above. Thank you!

Yes I did, and no I wouldn't dare learn stats from you, but you're welcome to learn from me.

No where in my statement did i agree with you. Its simple, compare the top 10 for example. If you look at the outlier in the top percentile for the race on both womens and mens, you will find that the mens field has a smaller stadard deviation away from outlier than the womens field. Which means that the mens field is much tightly packed

Also in terms of closeness to world record pace if you look at mens field compared to womens you see that the womens field has a larger disparity away from the record and the men are close and within 2 standard deviations for the mens field the elite guys are closer to record pace than womens field is. If you run multivariate analysis you also find that finding significance is tough for the womens field if you take say the top 100 because of the gaps in time, meaning the sample is not normal. The mens field has significance.

If you are a stat guy then you understand that significance does matter even when test is physical in nature i hope?