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How to mathematically separate the wheat from the chaff


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11 hours ago, Mendoza said:

 

Good question.

 

My best guess at the moment:  Maybe it's something to do with the expectation of seeing Bigfoot in some states but not so much in others.  It's been broadcast in various media for a few decades now that Bigfoot's range is concentrated in the Pacific Northwest.  So that's where people who haven't delved deeply into the subject (i.e., most people) would most expect to see Bigfoot.  Perhaps a person catching a glimpse of a large, hairy, and (at least momentarily) bipedal creature--a description that fits the black bear as well as Bigfoot--is more likely to believe that what they saw was Bigfoot and not merely a bear when the location is in one of the Pacific Northwest states, as opposed to the Group A' states where perhaps it's less likely that this notion would occur to them.

 

This would involve some function of the term p_e in Glickman's equation.  It's one of the less well-understood terms in the equation.  I may be able to look into this term more in the future, which would help me to test the hypothesis that expectation of seeing Bigfoot in certain states makes black bear misidentification more likely in those states.

 

I'm a firm believer that the eye witness is one half of the equation. With that said I can assure you that the legend of Bigfoot is just as alive in Denver or Missoula or Boise or Salt Lake City as it is in Seattle or Portland.

 

Are numbers of sighting lower in most inland Rocky Mtn areas? Absolutely! But remember there are a lot less people to report a sighting as well.

 

But I'm not even sure how you would quantify misidentified sightings vs. legitimate ones unless the area was completely devoid of Bears. Which is not the case in most lush areas of the PacNW. In fact I would argue that north Idaho has just as many black bears as western Washington. 

 

Goggle the ITR or Inland Temperate Rainforest. This area has some of the last intact ecosystems in North America.

Here is a map.

IMG_0464.JPG

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On ‎1‎/‎5‎/‎2017 at 8:41 AM, BigTreeWalker said:

I agree with WV FOOTER, bears and bigfoot require the same habitat. It's not faulty or wrong to assume that. Bigfoot have to eat, there are reports of what they eat and it's not wrong to assume they are omnivores just as bears are. So looking at it logically and from a wildlife biology standpoint, bears and bigfoot would have the same habitat requirements. That definitely is the case in the state of Washington. But I guess that supports the argument that all bigfoot are misidentified black bears. ;)

 

As to the affect of population densities by county, I'm sure you will see a difference in the conclusions. For example, King County in WA has a density of 913 people /sq mi., Skamania County has a density of 7/sq mi. According to the SSR, King County has 40 sightings and Skamania 39. In other words almost identical amount of sightings. That will definitely affect the outcome of the calculations. 

 

One other point is in your example of the deer in 20 sq mi. You would get the same results if you had one person and 20 deer. So without knowing bigfoot population densities there is really no way to correctly estimate the frequency of sightings in any given area. Regardless of knowing our population densities. The sightings in counties with lower human population densities would actually support the argument for higher bigfoot densities. Or more hoaxers in those counties, which I think is the opposite of what your numbers are showing. 

 

As I understand it, you're defending the assumption that Bigfoot and black bears have the same habitat requirements based on their diets.

 

Your understanding of Bigfoot's diet appears to be based on reports of what they're eating.  I can then ask the question:  What if these reports are hoaxes, misinterpretation, or in some other way wrong?  (For the record, I'm not saying they are wrong, I'm saying that we can't know for certain without physical evidence--evidence that would possibly make this whole discussion academic.)

 

Further, even if we admit the assumption that Bigfoot is an omnivore, I would argue that there are different levels of "omnivorousness."  There are animals that literally eat just about anything, and those that eat most things, and those whose diet revolves around certain foods but who can resort to alternatives when necessary.  I submit that we don't know which of these categories Bigfoot fits into.  Consequently, we don't know what habitats serve Bigfoot's needs.  For example, maybe one can handle habitats that the other can't.

 

We also have the issue of competitive exclusion.  Species that require the same resources actually tend to not share habitat, because one will eventually out-compete the other.

 

A useful example might be Bigfoot and grizzly bears.  There are a number of reports of Bigfoot eating many of the same things grizzly bears are known to eat.  Based on this, you might argue that Bigfoot and grizzly bears would be expected to share habitat.  But what I actually find is that in general there are fewer Bigfoot sightings in the states where you have more grizzlies.

 

On your last point, I encourage you to revisit my paper or my summary of the conclusions, as I haven't tried to estimate sighting frequency at all.  The frequency of Bigfoot sighting reports in each state is already known from the BFRO database and others.  What I tried to determine is whether or not sighting reports correlate with human population density (among other correlations), which is what we would expect if actually present humans are actually reporting actual sightings of an actual animal that is actually there to be seen.

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Negative. Big Tree Walker is boots on the ground. You should check out his bone study in the research group area on this forum.

 

Grizzlies vs Sasquatch? Grizzlies can live in more arid open regions, absolutely. But look at the two recovery zones in Washington state. Selkirk Mtns and North Cascades. These are wet wet lush areas. Even in many parts of Montana and Idaho such as the Yaak river drainage. Wet.

 

My estimation is there are less Bigfoot reports in Griz habitat because there are LESS people to make the report. Griz are a wilderness species. They tolerate human activity much less than black bear do.

 

I would actually argue that the closer you get to populated areas such as Portland and Seattle the more mis id's you get. Urban folks typically do not spend as much time in the woods. And it's hard to believe that there are that many Squatch living amongst heavy human populations with out us already having hard proof of their existence because one didn't get smashed by a garbage truck. If a logger from Libby Mt. Tells me he saw a bear or a Sasquatch I tend to believe it, and trust in his witness account.

 

I tend to think remote intact ecosystems are the places to be looking. Grizzlies being present is a good sign that you have found such a place. So are wolverines, Lynx and caribou. Turkey and Whitetail? Not so much.

 

With all that said? I like the fact your new to the forum and have rolled up your sleeves and are attempting to crunch the numbers. This field needs more of you and I sincerely mean that.

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Mendoza, I am in agreement with Norse on your efforts and find them interesting or else I wouldn't have read both you posted. But I do know it's not that simple. As Norseman said I do a lot of boots on the ground. There is field evidence that bears and bigfoot do coexist and utilize the same habitat. They probably avoid each other just as bears avoid us. With the feeding evidence we've found I could argue that they are carnivores. But that's going too far in the other direction. I think that are primates and and feed somewhat like we or chimps do. As far as two species using the same habit with similar overlapping feeding behavior deer and elk are a good example. We can also throw in moose in grizzly country. Grizzlies and black bears also have similar feeding behavior and utilize the same habitat. So I am basing my observations on experience and common knowledge about these other species. 

 

You did use statewide sightings in your calculations. I just moved it down to the county level to show the problems with trying to correlate sighting reports with our population densities. I'm with Norse in the thought that rural people are not going to mistakenly ID the various animals that they are already familiar with. I also agree with your conclusions of people from high population areas (city folks mostly), they do frequently misidentify various animals. That is from personal experience too. 

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I was curious about how the distribution of BFRO reports normalized by population density will change if we look at the data by county instead of by state.

 

Averaging data can sometimes lead to flaws in interpretation (like the statistician who drowned in a river with an average depth of 3 ft). 

Thus, I wanted to check if population density (when aggregated for the full state) was skewing the data.

I decided to explore the data looking at only two states (Washington and Florida).

I selected those because WA was in group A and FL was in group B (of Mendoza’s paper); and I wanted to explore very different states.

 

All BFRO cases by county came from the BFRO website.

Population data by county came from 2010 census.

Land area in square mile came from state government statistics.

 

The first graph below shows the cumulative distribution of BFRO reports (normalized per county population density) for the 39 counties in Washington.  The average from the counties (0.63) is much lower than the state average (6.08).  The chart shows that the distribution is skewed by a few counties with high number of normalized BFRO reports.

 

Attached is a Washington County map so that folks can see where Skamania and Okanogan counties are located (2 of the outliers).

 

The second graph shows the cumulative distribution of BFRO reports (normalized per county population density) for the 67 counties in Florida.  The average from the counties (0.03) is again much lower than the state average (0.88).  The chart shows that the distribution is skewed by a few counties with high number of normalized BFRO reports.  The Florida state average is a mathematical artifact and is not even within the scale of the counties distribution.

 

Attached is a Florida County map so that folks can see where Liberty and Levy counties are located (2 of the outliers).

 

The summary table compares the statistics of WA to FL and calculates the ratio of the averages. The county average was also calculated using population weighted average and land area weighed average basis instead of simple averaging of all counties.

Just as Mendoza had calculated beforehand using state average statistics, WA has a much higher number of BFRO reports (normalized by population density) than Florida.  The state average data showed a ratio of 7 to 1.  If we use the county average we get a ratio of 18.  Thus, WA indeed has more reports per population density than FL by a large number.

 

In doing this work, I realized that there are some issues/problems with the data that might impede drawing hard conclusions when comparing data from different states.  Some of these issues are:

  1. Number of BFRO reports for each state has different time horizons.  Some data collection started early in some states (60’s) and late in others.    Maybe reports should be included in the analysis for a limited but consistent time period (for example 30 years)?
  2. The amount of effort and staff to collect/investigate reports in FL might be different than in WA at different time periods.  Thus, the WA and FL databases might not be representative samples.
  3. If BF is a real creature and lives it both WA and FL, its population density might be totally different in FL than in WA because of different habitat/climate/food sources.  Thus, number of BFRO reports per human population density could be lower in FL than in WA because the density of BF population is much lower than in FL.
  4. Human population changes over time while the BFRO reports can go back to the 60’s in some states.  I am not sure if this dynamic affects much the statistical inferences. For example, I took human population data from 2010 census but some WA BFRO reports include data from the 60’s.

Washington BFRO Distribution by County.png

Washington Counties Map.png

Florida BFRO Distribution by County.png

Florida Counties Map.png

WA and FL BFRO Report Statistics Summary.png

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On ‎1‎/‎5‎/‎2017 at 11:50 AM, SWWASAS said:

While I would agree as a generality, when my first research area was active, and when I started seeing frequent BF footprints, I stopped finding bear footprints.   Could have been a coincidence but just guessing I think BF scares black bear out of an area.  Makes me wonder if black bear are BF prey too.   

 

It seems there's some variation among what the field researchers are reporting:

 

1.  Bigfoot and black bears coexisting in close proximity

2.  Bigfoot and black bears in close proximity but wary of each other

3.  Bigfoot and black bears actively avoiding each other

 

I need to make it clear what "no correlation" between Bigfoot sightings and black bear population density means as it pertains to my analysis.  "No correlation" does not mean the ranges of the two species never overlap--that would instead be a strongly negative correlation (specifically -1 in the case of absolutely no overlap).  Similarly, if the ranges of the two species exactly coincide, there would be a correlation of +1.  When there's no significant correlation, that means the ranges of the two species overlap roughly as often as not.

 

The following are rough back-of-the-envelope estimates of the degree of overlap of the two species' ranges that would be sufficient to produce the correlations I observed in the data:

Group A:  85% overlap OR significant misidentification of black bears as Bigfoot

Group A':  36% overlap

 

And, for good measure, corresponding estimates for brown bears and Bigfoot:

Group A:  28% overlap

Group A':  38% overlap

 

I believe the amount of overlap I'm thinking about is sufficient to account for what's been observed by the field researchers as well.

 

Someone might want to compare environments in Groups A and A' and see if there is any ecological reason why there might be more overlap between Bigfoot range and black bear range in Group A.  My thinking has been that there is no good reason why there would be more overlap there, and therefore misidentification of black bears as Bigfoot is responsible for the heightened correlation instead.  But I'm open to being convinced otherwise.

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12 hours ago, Mendoza said:

Someone might want to compare environments in Groups A and A' and see if there is any ecological reason why there might be more overlap between Bigfoot range and black bear range in Group A.

 

Dr Grover Krantz explained this a long time ago.

 

Black bears are diurnal. Bigfoot is thought to be nocturnal, so even though their ranges overlap, they operate at different times of the day, thus allowing them to share resources and coexist.

 

There are many other species who would normally compete for resources, but time-share instead.

 

I enjoyed reading your analysis. Thanks for posting it!

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12 hours ago, Mendoza said:

 

It seems there's some variation among what the field researchers are reporting:

 

1.  Bigfoot and black bears coexisting in close proximity

2.  Bigfoot and black bears in close proximity but wary of each other

3.  Bigfoot and black bears actively avoiding each other

 

 

Coexistence and avoidance are not mutually exclusive. You split that into 3 separate categories which wasn't necessary. It's just different ways of saying the same thing. I would guess the problem is your idea of what close proximity is. From personal experience elk and deer actively avoid each other though they utilize the same habitat and food sources. It's more like an out of sight out of mind type of avoidance. Something else, at least in the PNW, most of the time in forest situations it's just as hard to find evidence of bears in an area as it would be to find bigfoot evidence. The difference being that bears aren't as active at avoiding humans. Bear scat is the most likely evidence you'll find. 

My thoughts are there is actually more overlap in the A' states than is being allowed for. Black bears utilize the same general habitat wherever they are found in North America. They like forests. Thick, thin, young, old, wet or drier, deciduous or conifer. They utilize available cover. Sounds kind of like bigfoot. ;)

 

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On ‎1‎/‎5‎/‎2017 at 10:16 AM, MIB said:

 

Arguably proportional to the total human population I suppose, but inferring concentrations of hoaxes based on concentration of human population requires an illogical, invalid leap.   Your hypothetical hoaxer on the 30th floor can, with equal ease, fabricate a report from a Chicago suburb, a Florida swamp, or the mountains of Idaho.   All the hoaxer needs is to be well read regarding bigfoot and familiar with the location, possibly via some past vacation they too, but possibly even just via maps and photographs.   

 

MIB

 

On ‎1‎/‎6‎/‎2017 at 8:12 AM, JustCurious said:

I don't think comparing population density to bigfoot sightings can really be a solid measure.  You have too many sightings by people who are not local to a geographic area (tourists, hunters, etc.) and unless you know the identity of the sightee, you could have one person with multiple reports skewing results at the county level.  Consider  Wyoming - the state population is about 584,000, but Yellowstone gets 4 million visitors a year.  Other examples are Wisconsin Dells, which has a native population of about 5000, but gets 4 million visitors a year.  How do you factor in those influxes?

 

In my opinion, you have to pick something more static to make comparisons.  Land area to sightings reports would be static, but doesn't take into account whether there are witnesses present to make a sighting.  A tough one with no solid answer.

 

I'm going to respond to both of these posts at the same time since they both basically raise the same issue.

 

Sighting reports by non-locals is indeed a potential source of error in this analysis.  These two posts are valuable as they point out another limitation of the methodology used.

 

To address the first concern, the assumption that fabricated reports are generated by people in near proximity to the location in which the false sighting purports to have taken place introduces an error of unknown degree into the analysis.  However, I think it's a reasonable assumption that non-local hoaxes are the exception and not the rule, and not frequent enough to invalidate my analysis.  (Of course, I'm open to an analysis of reports that proves this assumption wrong.)  Moreover, non-local hoaxes being perpetrated at random geographically would be more likely to reduce the correlations observed rather than produce them by sheer luck.

 

The alternate scenario of a well-informed hoaxer seems like an especially isolated case, and equally unlikely to invalidate this analysis.  To deliberately poison the database with enough hoaxes to skew the data in a specific way, to the degree needed to produce the observed correlations, would require a great deal of effort by a single hoaxer, or a concerted effort by a team of hoaxers.  We're talking about dozens, if not hundreds of fabricated reports, by the same people.  I just don't see this happening in reality.  Furthermore, this level of organized hoaxing would be enough to invalidate the database as a whole, such that we couldn't learn anything from it.  In fact, I suspect that the sheer number of hoaxes needed to produce the observed correlations is more hoaxing than I would expect given the correlations I did observe.

 

Regarding the second issue raised, that even authentic reports might be submitted by non-locals, I think it's fair to say that the majority of people do most of their outdoor activities in the areas around their primary residence.  Yes, people travel and take scenic vacations, but I suspect that the ratio of people traveling to people staying local at any given time is relatively small.  Consequently I would expect probability alone to favor sightings by locals to a relatively high degree.  (Again, I'm open to analysis showing otherwise.)

 

National parks and other tourist attractions do present localized exceptions to this logic, but my analysis was done on the state level and most states have popular tourist attractions somewhere within their boundaries, so I suspect that this would result in more equalization of this effect than you would expect at first glance.

 

Finally, I would expect that sightings by non-locals would have a randomizing effect on the data, which would again actually minimize strong correlations in the data rather than produce them.

 

I would, however, be interested in any analysis using a similar methodology that factors tourism data in with population data.

 

To summarize, I do recognize that the two effects mentioned would introduce error into this analysis, and so I appreciate them being pointed out, but I don't believe the error involved is significant enough to invalidate the results.

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Interesting thoughts.    A large portion of the economy, outside the city limits, in my area is tourism and very much of that is from non-residents.    A good portion of those people live in-state but reside 4+ hours away to the north, farther away than many out of state residents who only live 1-3 hours to the south.     We do indeed have major tourist draws which put people into the outdoors including Crater Lake National Park and the Pacific Crest Trail that draw heavily from out of state.   Others tend to draw more regionally like people fishing the Rogue River who, though some are international visitors, many are in-state residents from other parts of the state.

 

Outside of those areas, and often within them, too, most of the reports come from local people doing local people things ... or people PURPORTING to be local people doing local people things.   Among the things are driving to/from work, driving to/from kids' sporting events, snowmobiling, cutting Christmas trees, picking berries, or just letting the dog out in the middle of the night.   I say "purporting" deliberately though.   Examples that come to mind are videos of reports being read on youtube like the "Cascade Bigfoot" entries discussed here on the forum, at least two of which I can "prove" are false ... whoever made the report has not been to the site 'cause they got critical details wrong, looks good if you only go by maps, but the maps are imprecise causing the hoaxer to assume falsely and foul up the hoax.

 

I guess the point is any sort of deliberate leveling / averaging increases error.   The only debatable part is how much.    You offer no evidence, only opinion, regarding how much.   I'm in the same boat.   I think it is greater than you are allowing for but it is only a local's seat of the pants feel, I can't give you firm numbers to plug into a formula, only a sense you're getting it wrong.

 

MIB

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On ‎1‎/‎6‎/‎2017 at 11:46 AM, norseman said:

 

I'm a firm believer that the eye witness is one half of the equation. With that said I can assure you that the legend of Bigfoot is just as alive in Denver or Missoula or Boise or Salt Lake City as it is in Seattle or Portland.

 

Are numbers of sighting lower in most inland Rocky Mtn areas? Absolutely! But remember there are a lot less people to report a sighting as well.

 

But I'm not even sure how you would quantify misidentified sightings vs. legitimate ones unless the area was completely devoid of Bears. Which is not the case in most lush areas of the PacNW. In fact I would argue that north Idaho has just as many black bears as western Washington. 

 

Goggle the ITR or Inland Temperate Rainforest. This area has some of the last intact ecosystems in North America.

Here is a map.

IMG_0464.JPG

 

Assuming, as you say, that the legend of Bigfoot is just as alive in the states I've placed in Group A' as it is in the states I've assigned to Group A, I still question whether or not the people in the Group A' states are as expectant of having Bigfoot in their own state as are those in the Group A states.  An example of a similar situation would be the way that everyone knows about the Loch Ness Monster, but nobody expects to see it in Lake Okeechobee.

 

My methodology (technically, Glickman's methodology, to give credit where it's due) is actually based on the fact that there would be fewer sighting reports where there are fewer people in the right place and time to experience the sighting and report it, so I'm not sure how your mention of that fits into the rest of your critique of my analysis.  I'd appreciate it if you could clarify why you're mentioning that in the context of your evaluation of what I've done.

 

I don't believe we can actually quantify misidentification vs. real Bigfoot sightings, but I do believe the correlations I observed in the data gives us reason to suspect a significant contribution from black bear misidentification in some states.

 

By the way, for what it's worth, my similar analysis in 2005 concluded that misidentification of black bears does not contribute in any significant way to the Bigfoot phenomenon.  I was perfectly happy with this conclusion as it made it easier to refute one of the means by which Bigfoot disbelievers seek to dismiss the entire phenomenon.  However, I revised this conclusion after the present analysis because the updated data produced different results.

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On ‎1‎/‎7‎/‎2017 at 11:45 AM, norseman said:

I would actually argue that the closer you get to populated areas such as Portland and Seattle the more mis id's you get. Urban folks typically do not spend as much time in the woods. And it's hard to believe that there are that many Squatch living amongst heavy human populations with out us already having hard proof of their existence because one didn't get smashed by a garbage truck. If a logger from Libby Mt. Tells me he saw a bear or a Sasquatch I tend to believe it, and trust in his witness account.

 

I find that idea very interesting, because it might be an alternative explanation for the increased black bear misidentification in Group A states that I concluded from my analysis.  I wonder how the ratio of urbanites to total population in Group A compares with the urbanite ratio in Group A'...

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On ‎1‎/‎7‎/‎2017 at 6:17 PM, Explorer said:

I was curious about how the distribution of BFRO reports normalized by population density will change if we look at the data by county instead of by state.

 

Averaging data can sometimes lead to flaws in interpretation (like the statistician who drowned in a river with an average depth of 3 ft). 

Thus, I wanted to check if population density (when aggregated for the full state) was skewing the data.

I decided to explore the data looking at only two states (Washington and Florida).

I selected those because WA was in group A and FL was in group B (of Mendoza’s paper); and I wanted to explore very different states.

 

All BFRO cases by county came from the BFRO website.

Population data by county came from 2010 census.

Land area in square mile came from state government statistics.

 

The first graph below shows the cumulative distribution of BFRO reports (normalized per county population density) for the 39 counties in Washington.  The average from the counties (0.63) is much lower than the state average (6.08).  The chart shows that the distribution is skewed by a few counties with high number of normalized BFRO reports.

 

Attached is a Washington County map so that folks can see where Skamania and Okanogan counties are located (2 of the outliers).

 

The second graph shows the cumulative distribution of BFRO reports (normalized per county population density) for the 67 counties in Florida.  The average from the counties (0.03) is again much lower than the state average (0.88).  The chart shows that the distribution is skewed by a few counties with high number of normalized BFRO reports.  The Florida state average is a mathematical artifact and is not even within the scale of the counties distribution.

 

Attached is a Florida County map so that folks can see where Liberty and Levy counties are located (2 of the outliers).

 

The summary table compares the statistics of WA to FL and calculates the ratio of the averages. The county average was also calculated using population weighted average and land area weighed average basis instead of simple averaging of all counties.

Just as Mendoza had calculated beforehand using state average statistics, WA has a much higher number of BFRO reports (normalized by population density) than Florida.  The state average data showed a ratio of 7 to 1.  If we use the county average we get a ratio of 18.  Thus, WA indeed has more reports per population density than FL by a large number.

 

In doing this work, I realized that there are some issues/problems with the data that might impede drawing hard conclusions when comparing data from different states.  Some of these issues are:

  1. Number of BFRO reports for each state has different time horizons.  Some data collection started early in some states (60’s) and late in others.    Maybe reports should be included in the analysis for a limited but consistent time period (for example 30 years)?
  2. The amount of effort and staff to collect/investigate reports in FL might be different than in WA at different time periods.  Thus, the WA and FL databases might not be representative samples.
  3. If BF is a real creature and lives it both WA and FL, its population density might be totally different in FL than in WA because of different habitat/climate/food sources.  Thus, number of BFRO reports per human population density could be lower in FL than in WA because the density of BF population is much lower than in FL.
  4. Human population changes over time while the BFRO reports can go back to the 60’s in some states.  I am not sure if this dynamic affects much the statistical inferences. For example, I took human population data from 2010 census but some WA BFRO reports include data from the 60’s.

 

Very interesting work!

 

If I'm reading correctly, then you've determined that breaking down the human population density by county basically confirms comparisons in population density made on the state level (at least in the case of Washington and Florida).  This is more or less what I expect--that an analysis done on the county level is probably going to affirm, for the most part, what I've already done on the state level.  The degree of new information I would expect from a county-by-county analysis would be somewhere along the lines of finding that not all of Washington is Bigfoot territory (which I think is already quite obvious) and that maybe Bigfoot also has a presence in Florida (a definite possibility), as opposed to radically different findings that might suggest there's nothing in WA after all and FL turns out to be crawling with Bigfoot.

 

I do notice that the ratio of population density between the two states changed when you broke it down to the county level.  That does suggest to me that there is a lot of new information to be gained if I repeat my analysis at the county level.  This of course will take a while, because there are far more counties than states, which makes the county-level analysis more demanding in terms of raw number-crunching by a couple of orders of magnitude.

 

...But I'm up to the challenge.  I've got a few other works in progress to finish up first, but stay tuned.

On ‎1‎/‎9‎/‎2017 at 1:26 AM, gigantor said:

 

Dr Grover Krantz explained this a long time ago.

 

Black bears are diurnal. Bigfoot is thought to be nocturnal, so even though their ranges overlap, they operate at different times of the day, thus allowing them to share resources and coexist.

 

There are many other species who would normally compete for resources, but time-share instead.

 

I enjoyed reading your analysis. Thanks for posting it!

 

This is true, but I don't see how this explains the difference in the correlations between the observed Bigfoot sighting report frequencies and black bear population densities in Groups A and A'.  Time-sharing of habitats could explain an overlap in ranges (and thus the observed high level of correlation between the two species) in Group A, but then why is this same correlation (with the consequent inference of overlap in ranges) not observed in Group A'?

 

Are they time-sharing a habitat in Group A but not in Group A'?  If so, why?  There must be some major ecological difference if this is the case.

On ‎1‎/‎9‎/‎2017 at 2:40 AM, BigTreeWalker said:

 

My thoughts are there is actually more overlap in the A' states than is being allowed for. Black bears utilize the same general habitat wherever they are found in North America. They like forests. Thick, thin, young, old, wet or drier, deciduous or conifer. They utilize available cover. Sounds kind of like bigfoot. ;)

 

 

I'm open to being convinced of that.

 

But the first question I would need answered is, why is the expected correlation if this is true (a positive correlation between black bear population density and Bigfoot sighting frequency) not observed in these states?

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On ‎1‎/‎11‎/‎2017 at 10:42 AM, MIB said:

Interesting thoughts.    A large portion of the economy, outside the city limits, in my area is tourism and very much of that is from non-residents.    A good portion of those people live in-state but reside 4+ hours away to the north, farther away than many out of state residents who only live 1-3 hours to the south.     We do indeed have major tourist draws which put people into the outdoors including Crater Lake National Park and the Pacific Crest Trail that draw heavily from out of state.   Others tend to draw more regionally like people fishing the Rogue River who, though some are international visitors, many are in-state residents from other parts of the state.

 

Outside of those areas, and often within them, too, most of the reports come from local people doing local people things ... or people PURPORTING to be local people doing local people things.   Among the things are driving to/from work, driving to/from kids' sporting events, snowmobiling, cutting Christmas trees, picking berries, or just letting the dog out in the middle of the night.   I say "purporting" deliberately though.   Examples that come to mind are videos of reports being read on youtube like the "Cascade Bigfoot" entries discussed here on the forum, at least two of which I can "prove" are false ... whoever made the report has not been to the site 'cause they got critical details wrong, looks good if you only go by maps, but the maps are imprecise causing the hoaxer to assume falsely and foul up the hoax.

 

I guess the point is any sort of deliberate leveling / averaging increases error.   The only debatable part is how much.    You offer no evidence, only opinion, regarding how much.   I'm in the same boat.   I think it is greater than you are allowing for but it is only a local's seat of the pants feel, I can't give you firm numbers to plug into a formula, only a sense you're getting it wrong.

 

MIB

 

I agree that some amount of error is introduced every time you simplify an analysis by making a broad assumption for lack of data (or even solely for the sake of simplicity, as overcomplicating things can bring its own errors).

 

I also agree that this particular simplifying assumption is purely my opinion at this point.  I think it's a well-reasoned opinion, although you have every right to say the same thing about yours--and you may be getting it right and I may be getting it wrong.

 

There's got to be data out there, and some scientific methodology, to help resolve the question of sightings by non-locals and how much they impact the dataset.  Ideas, anyone?

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