<p>Let's have a closer look at the p-values in your analysis. By far the most significant effect in your model is the one captured by "groupold", which indicates a significant difference between pre- and post-1972 temperatures. This points to the climate change effect, as noted in John's answer.</p>

<p>The next most significant factor we have is "countdown", which indicates, not totally surprisingly, that temperatures tend to increase in the end of June and cool off into July.</p>

<p>Finally, we have the interaction term, which is orders of magnitude less significant than the other factors. The most reasonable explanation for this is that climate change effects are most notable in the extremes of weather. As climate change raises the overall average temperature, it also pushes the extremes higher and lower. Climate change has a significant effect on temperatures, but that effect is even more pronounced in the height of summer.</p>

<p>So no, the festival organizers haven't been using their WeatherSetter 9000 to make the last week of June warmer than average. Rather, climate change has made weeks since 1972 warmer than average, seasonal cycles have always made the last week of June warmer than average, and the climate change effect is especially strong at the extremes of weather.</p>

CC BY-SA 4.0
2019-04-26T19:24:46.713Z
Brilliant! This is absolutely the most plausible response, in my view, that I have received on this question. Thank you for satisfying my curiosity after 10 months of wondering 8-)
Ashish
CC BY-SA 4.0
2019-04-26T19:38:42.267Z
@Ashish It would be interesting to test this hypothesis for other locations/times of year, to see if the interaction between climate change effects and seasonal temperature cycles remains consistent. If my supposition is correct, we should see this effect occur in other locations, but be less pronounced during months of less extreme weather.
Nuclear Hoagie
CC BY-SA 4.0
2019-04-26T15:36:53.820Z
2019-04-26T15:44:46.477Z
2019-04-26T15:44:46.477Z
Nuclear Hoagie
<p><strong>You are asking the wrong question</strong>. The real question is why some podunk single city festival and not other larger more costly events, like space launches, national holidays, military campaigns, hurricanes, or a million other festivals. The conspiracy theorists likely picked that festival through data dredging, which means you are not going to disprove it by looking at the same data. If you compare enough festivals you will find some in which the weather does weird things, it is the law of large numbers in action.  </p>

<p><strong>A festival that started in the 1970's is perfect for data dredging, there is a change in the underlying forces of weather in the 1970's.</strong> The undeniable warming trend in global climate starts around the 1970's so if you divide your data sets before and after any point around that time the data will always show a significant difference, unless you correct for that shift. Without correcting for it all you are detecting is climate change, a known factor,n anthropomorphic factor at that. So really humans are altering the weather, just not in the way your friend thinks. </p>

<p><a href="https://i.sstatic.net/WKwsi.jpg" rel="nofollow noreferrer"><img src="https://i.sstatic.net/WKwsi.jpg" alt="enter image description here"></a></p>

<p><a href="https://i.sstatic.net/LVe7C.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/LVe7C.png" alt="enter image description here"></a></p>

CC BY-SA 4.0
2019-04-25T12:54:07.537Z
The question was asked specifically about the last weekend of June, temperature rising and falling. This is unrelated to the long-term trend.
Gimelist
CC BY-SA 4.0
2019-04-25T14:52:35.193Z
but you are noting how this changes over many years, you note the behavior of the weather changes in 1972 that is the significant factor. the fact the average temprature is rising will impact this. temprature rising and falling from day to day is completely normal.
John
CC BY-SA 4.0
2019-04-26T03:52:25.237Z
The long term trend is controlling the only significant factor, that the data changes behavior in the 1970's nothing else in noteworthy. the rise and fall is just statistical noise, it's not significant. it is pure data dredging. like drawing the bullseye around a cluster of shots already fired and declaring superb accuracy.
John
CC BY-SA 4.0
2019-04-26T14:15:03.630Z
There was no data dredging. I tested one hypothesis. Every decision I made was strictly driven by theory (misguided theory nonetheless, but still not data dredging). You make it sound like I was testing all different years to split arbitrarily. And secondly, the day-to-day rise and fall is not just statistical noise--that's precisely the point of the post and the slope was non-zero in the "conspiracy-ridden years" and 0 in the "innocuous years". If it were noise, it would cancel out to be a slope of 0 in all of the years.
Ashish
CC BY-SA 4.0
2019-04-26T14:23:23.290Z
And in regard to your first point, obviously this is a spurious effect. I am just trying to determine why I found it. This is not a common theory that conspiracy theorists found by data dredging. Weather manipulation in general is a known theory, but in regard to this festival this was just something that my friend with absolutely no data analysis skills came up with based on his anecdotal experience and memory.
Ashish
CC BY-SA 4.0
2019-04-26T15:13:40.853Z
The data dredging was not on your part, but by the people who picked that festival to focus on. if someone examines enough festivals they will find some with odd looking behavior by pure chance. You are misunderstanding what i mean by noise the day to day variation is noise, the change in the variation across the entire time scale is do to climate change, the underlying trend changes in the 1970's. Even if there were no dredging the change in yearly behavior is completely explained by this.
John
CC BY-SA 4.0
2019-04-24T13:31:41.210Z
2019-04-26T15:20:38.483Z
2019-04-26T15:20:38.483Z
John
<p>I have a conspiracy-prone friend who believes that people are controlling the weather to ensure good weather for a large, annual festival (last weekend in June in San Francisco). As a scientist, (not earth science), I was interested in testing this hypothesis. So..I built 2 linear models--one representing the days leading up to the festival, one representing the days following the festival-- where the I regressed max daily temperature onto the number of days away from the festival. Lo and behold, indeed the temperature did appear to rise, peak on the weekend of the festival, and then drop afterwards! Next I figured, it must just be a natural trend, irrespective of local festivities (and weather-controlling overlords). So next, I included data from the years before the festival began (1972), made two groups (pre-festival and post-festival) and included a days_away_from_festival*group interaction term. To my great surprise, the interaction was significant! Testing only the group dated before the festival began, did not yield significant results.</p>

<p>And so! My question is... can any of you experts explain this by means other than a statistical coincidence/wealthy weather gods? Why might the temperature in San Francisco tend to rise in the week leading up to the last weekend in June, and then fall afterwards--but for this effect not to be present in the years before 1970'sish?</p>

<p>EDIT: Here are my two models. </p>

<p>The first model represents the days leading up to the festival and days are indicated by the countdown variable, (e.g. Friday = -1, Saturday = 0) There are 5 days prior included in the model. The group variable refers to the fact that I split the years into two groups, old:(1942-1971) and new:(1972-2017). TMAX is the maximum temperature of the day.</p>

<p><a href="https://i.sstatic.net/bjN0g.png" rel="noreferrer"><img src="https://i.sstatic.net/bjN0g.png" alt="pre festival model"></a></p>

<p>The second model is the same except it represents the days following the festival.
<a href="https://i.sstatic.net/urNr3.png" rel="noreferrer"><img src="https://i.sstatic.net/urNr3.png" alt="post festival model"></a></p>

<p>As you can see, after 1972 the temperature seems to rise, peak during the festival in the last weekend in June, and then fall immediately after. However, this effect was not present prior to 1972.</p>

<p>EDIT2: The data came from <a href="https://www.ncei.noaa.gov" rel="noreferrer">https://www.ncei.noaa.gov</a> and the station is San Francisco Downtown</p>

CC BY-SA 4.0
2018-06-29T07:13:23.720Z
Two ideas. First, the station seems to have moved around a bit, including in 1973 (https://www.ncdc.noaa.gov/cdo-web/datasets/GHCND/stations/GHCND:USW00023272/detail). Second, global warming could be a confounding signal over those decades, as could increasing urban heat island effect. If possible, see if you get similar results a) from other urban sites unrelated to the festival or b) from relatively nearby rural sites.
Jareth Holt
CC BY-SA 4.0
2018-06-29T13:38:03.253Z
You do know that this is futile, right? It's a fine exercise for yourself, but you're not going to "prove a conspiracy believer wrong" with data.
Jan Doggen
CC BY-SA 4.0
2018-06-29T15:23:33.607Z
Perfect example of Trump's Razor: Ascertain the stupidest possible scenario that can be reconciled with the available facts. Of course it's a conspiracy!
Tim Nevins
CC BY-SA 4.0
2018-06-29T17:28:41.180Z
Why the assumption that higher temperature means good weather?  Define "good".
jamesqf
CC BY-SA 4.0
2018-06-29T19:41:54.773Z
Try running similar tests on cities that don't have a festival.  If it's a conspiracy, it may be that SF is borrowing nice weather from neighboring towns, and that, say, Redwood City is getting rain on that weekend.
Sherwood Botsford
CC BY-SA 4.0
2018-07-02T16:23:35.063Z
To answer a few of the comments posed here... I did try with a couple other cities and was unable to find a significant effect in those cities. I don't think it could be caused by increased traffic because the temperature rises in the days leading up as well, before the festival begins.  Barry, yes.. if it is a conspiracy I'm grateful because the weather at this year's Pride was fantastic!
Ashish
CC BY-SA 4.0
2018-07-02T22:01:00.250Z
@TimNevins-There's no need to post that on this site. Go to Politics and fire at will.
Dunk
CC BY-SA 4.0
2019-04-23T00:57:46.713Z
How many days of bad weather do you actually get in San Francisco, especially in the summer. saying the weather was nice in San Francisco is like saying the weather was cold in antarctica.
John
CC BY-SA 4.0
2019-04-23T01:02:59.593Z
Did you consider that the data may have been manipulated for some inscrutable purpose? 
Keith McClary
CC BY-SA 4.0
2019-04-23T02:33:44.857Z
Although I'm not confident I fully understand your data analysis, the results sound exactly like what one would expect when testing a *post hoc* hypothesis developed from the very data one is using for the testing: the significance is grossly inflated and there is an interaction with a temporal indicator. If I'm correct, then you have little chance of changing your friend's opinion. You could take similar datasets for other cities, dredge them to develop comparable hypotheses, and perform *post hoc* tests to show you can *always* find a "significant" result. *All* cities control their weather!
whuber
CC BY-SA 4.0
2019-04-24T03:36:28.610Z
@John The point is that the temperature rises for the festival. And actually SF has notoriously gloomy summers (maybe not by other cities standards, but by our standards we do!)
Ashish
CC BY-SA 4.0
2019-04-24T03:37:04.870Z
@KeithMcClary That sounds like a conspiracy in itself. The data is from the official weather reports -- not someone trying to promote the conspiracy.
Ashish
CC BY-SA 4.0
2019-04-24T03:37:35.613Z
@whuber Great suggestion -- however, I tried this and did not find the effect in other major cities.
Ashish
CC BY-SA 4.0
2019-04-24T03:46:24.063Z
Ashish: What effect, exactly? The idea is not to find *this* effect, but to look for *any* effect: that is the correct way to evaluate whether this one is "significant" or not. If you are finding it problematic to make this actionable (what effect, exactly, would one look for?), you are not alone. This is the inherent difficulty with assessing patterns that are found after data collection: you need somehow to model the process that was used to find the pattern in the first place. Although that's not impossible, it's difficult--and is unlikely to provide evidence that will persuade your friend.
whuber
CC BY-SA 4.0
2019-04-24T12:56:28.320Z
@whuber actually looking for a specific effect IS the correct way to do science, just combing through the data for any effect is called Data Dredging and is not good science. The law of large numbers means you will almost always find an effect if you look long enough.
John
CC BY-SA 4.0
2019-04-24T13:05:18.993Z
Have you tried comparing it to Milankovitch cycles, or comparing it to the trend over the entire time and not just before and after. you can always generate a difference if you pick and choose where to divide the data. Aslo the temprature rises in late june is hardly surprising, it may even be why that date was chosen for the festival.
John
CC BY-SA 4.0
2019-04-24T13:17:28.193Z
honestly this probably belongs on the skeptic or statistics stack. You can also ask why they change the weather for some tiny festival in california and not presidential inaugurations, shuttle launches, military campaigns, or hurricanes. Likely the conspiracy theorists picked that festival because they found a random jump in the data there, which you can always find if you comb through enough data, and why data dredging is bad.
John
CC BY-SA 4.0
2019-04-24T13:48:40.227Z
@John Your first comment goes to the heart of the matter: I am suggesting that the "effect" described in this post may be the result of data dredging and nothing more.  One way to demonstrate that is to show that similar "effects" that are comparably statistically "significant" can be produced in any other comparable dataset.  Looking for *this* (likely spurious) pattern of temperatures at the end of June in any other dataset would not address this issue, nor would it advance scientific inquiry.
whuber
CC BY-SA 4.0
2019-04-26T14:24:00.543Z
@whuber I greatly respect your statistical prowess, but I think there may be a misunderstanding at how I reached this result. There was no data dredging. I was not looking for "any effect"--I was testing a single, specific hypothesis that was formulated before inspecting any data. My friend said that the temperature was artificially raised for the festival (operationalized as rising temperature in the preceding days and falling temp in the following days), and that is exactly the only thing that was tested. How is that data dredging?
Ashish
CC BY-SA 4.0
2019-04-26T14:57:48.257Z
Ashish, I am not trying to suggest you did any data dredging at all.  But isn't it clear that your friend formulated this hypothesis as a reaction to the data?  It's a *post hoc* hypothesis and needs to be evaluated as such.
whuber
CC BY-SA 4.0
2019-04-26T19:18:47.523Z
@whuber the hypothesis came as a result my friend living in San Francisco for a few years and noticing that the weather felt hotter during this festival. He did not look at climate records. Do climate scientists ever conduct any analyses that are not post hoc in this regard? It would take 50 years to test a single hypothesis. Further, the data in the model is primarily data from outside of the period of his anecdotal observation. Would you still consider that data dredging? And would you still say that if I fit the model with data before he lived in the city and it was significant?
Ashish
CC BY-SA 4.0
2019-04-26T19:31:38.133Z
"Noticing" is the classic example of a *post hoc* hypothesis.  You're allowed to notice: indeed, good scientists notice interesting things all the time.  What is invalid, though, is to claim that the very data or experiences that led to a hypothesis formally verify it.  This principle is enshrined in the requirement that research be *reproducible.*  The onus is not on you to refute your friend's hypothesis; the burden is on your friend to demonstrate reproducibility.
whuber
CC BY-SA 4.0
2019-04-27T06:59:17.857Z
@whuber true.... unfortunately my friend is not concerned with bothersome things such as "evidence" and "statistics"
Ashish
<temperature><models>
Debunking weather conspiracy with statistics - why does temperature rise then fall for last weekend in June in recent years
Ashish
# Debunking weather conspiracy with statistics - why does temperature rise then fall for last weekend in June in recent years

(Asked by: username_0 on 2018-06-28)

I have a conspiracy-prone friend who believes that people are controlling the weather to ensure good weather for a large, annual festival (last weekend in June in San Francisco). As a scientist, (not earth science), I was interested in testing this hypothesis. So..I built 2 linear models--one representing the days leading up to the festival, one representing the days following the festival-- where the I regressed max daily temperature onto the number of days away from the festival. Lo and behold, indeed the temperature did appear to rise, peak on the weekend of the festival, and then drop afterwards! Next I figured, it must just be a natural trend, irrespective of local festivities (and weather-controlling overlords). So next, I included data from the years before the festival began (1972), made two groups (pre-festival and post-festival) and included a days\_away\_from\_festival\*group interaction term. To my great surprise, the interaction was significant! Testing only the group dated before the festival began, did not yield significant results.

And so! My question is... can any of you experts explain this by means other than a statistical coincidence/wealthy weather gods? Why might the temperature in San Francisco tend to rise in the week leading up to the last weekend in June, and then fall afterwards--but for this effect not to be present in the years before 1970'sish?

EDIT: Here are my two models.

The first model represents the days leading up to the festival and days are indicated by the countdown variable, (e.g. Friday = -1, Saturday = 0) There are 5 days prior included in the model. The group variable refers to the fact that I split the years into two groups, old:(1942-1971) and new:(1972-2017). TMAX is the maximum temperature of the day.

[![pre festival model](https://i.sstatic.net/bjN0g.png)](https://i.sstatic.net/bjN0g.png)

The second model is the same except it represents the days following the festival.
[![post festival model](https://i.sstatic.net/urNr3.png)](https://i.sstatic.net/urNr3.png)

As you can see, after 1972 the temperature seems to rise, peak during the festival in the last weekend in June, and then fall immediately after. However, this effect was not present prior to 1972.

EDIT2: The data came from <https://www.ncei.noaa.gov> and the station is San Francisco Downtown

**Tags:** temperature, models

> **Comments:**
> **username_1**: Two ideas. First, the station seems to have moved around a bit, including in 1973 (https://www.ncdc.noaa.gov/cdo-web/datasets/GHCND/stations/GHCND:USW00023272/detail). Second, global warming could be a confounding signal over those decades, as could increasing urban heat island effect. If possible, see if you get similar results a) from other urban sites unrelated to the festival or b) from relatively nearby rural sites.  
> **username_2**: You do know that this is futile, right? It's a fine exercise for yourself, but you're not going to "prove a conspiracy believer wrong" with data.  
> **username_3**: Perfect example of Trump's Razor: Ascertain the stupidest possible scenario that can be reconciled with the available facts. Of course it's a conspiracy!  
> **username_4**: Why the assumption that higher temperature means good weather?  Define "good".  
> **username_5**: Try running similar tests on cities that don't have a festival.  If it's a conspiracy, it may be that SF is borrowing nice weather from neighboring towns, and that, say, Redwood City is getting rain on that weekend.  
> **username_0**: To answer a few of the comments posed here... I did try with a couple other cities and was unable to find a significant effect in those cities. I don't think it could be caused by increased traffic because the temperature rises in the days leading up as well, before the festival begins.  Barry, yes.. if it is a conspiracy I'm grateful because the weather at this year's Pride was fantastic!  
> **username_6**: @username_3-There's no need to post that on this site. Go to Politics and fire at will.  
> **username_7**: How many days of bad weather do you actually get in San Francisco, especially in the summer. saying the weather was nice in San Francisco is like saying the weather was cold in antarctica.  
> **username_8**: Did you consider that the data may have been manipulated for some inscrutable purpose?   
> **username_9**: Although I'm not confident I fully understand your data analysis, the results sound exactly like what one would expect when testing a *post hoc* hypothesis developed from the very data one is using for the testing: the significance is grossly inflated and there is an interaction with a temporal indicator. If I'm correct, then you have little chance of changing your friend's opinion. You could take similar datasets for other cities, dredge them to develop comparable hypotheses, and perform *post hoc* tests to show you can *always* find a "significant" result. *All* cities control their weather!  
> **username_0**: @username_7 The point is that the temperature rises for the festival. And actually SF has notoriously gloomy summers (maybe not by other cities standards, but by our standards we do!)  
> **username_0**: @username_8 That sounds like a conspiracy in itself. The data is from the official weather reports -- not someone trying to promote the conspiracy.  
> **username_0**: @username_9 Great suggestion -- however, I tried this and did not find the effect in other major cities.  
> **username_9**: username_0: What effect, exactly? The idea is not to find *this* effect, but to look for *any* effect: that is the correct way to evaluate whether this one is "significant" or not. If you are finding it problematic to make this actionable (what effect, exactly, would one look for?), you are not alone. This is the inherent difficulty with assessing patterns that are found after data collection: you need somehow to model the process that was used to find the pattern in the first place. Although that's not impossible, it's difficult--and is unlikely to provide evidence that will persuade your friend.  
> **username_7**: @username_9 actually looking for a specific effect IS the correct way to do science, just combing through the data for any effect is called Data Dredging and is not good science. The law of large numbers means you will almost always find an effect if you look long enough.  
> **username_7**: Have you tried comparing it to Milankovitch cycles, or comparing it to the trend over the entire time and not just before and after. you can always generate a difference if you pick and choose where to divide the data. Aslo the temprature rises in late june is hardly surprising, it may even be why that date was chosen for the festival.  
> **username_7**: honestly this probably belongs on the skeptic or statistics stack. You can also ask why they change the weather for some tiny festival in california and not presidential inaugurations, shuttle launches, military campaigns, or hurricanes. Likely the conspiracy theorists picked that festival because they found a random jump in the data there, which you can always find if you comb through enough data, and why data dredging is bad.  
> **username_9**: @username_7 Your first comment goes to the heart of the matter: I am suggesting that the "effect" described in this post may be the result of data dredging and nothing more.  One way to demonstrate that is to show that similar "effects" that are comparably statistically "significant" can be produced in any other comparable dataset.  Looking for *this* (likely spurious) pattern of temperatures at the end of June in any other dataset would not address this issue, nor would it advance scientific inquiry.  
> **username_0**: @username_9 I greatly respect your statistical prowess, but I think there may be a misunderstanding at how I reached this result. There was no data dredging. I was not looking for "any effect"--I was testing a single, specific hypothesis that was formulated before inspecting any data. My friend said that the temperature was artificially raised for the festival (operationalized as rising temperature in the preceding days and falling temp in the following days), and that is exactly the only thing that was tested. How is that data dredging?  
> **username_9**: username_0, I am not trying to suggest you did any data dredging at all.  But isn't it clear that your friend formulated this hypothesis as a reaction to the data?  It's a *post hoc* hypothesis and needs to be evaluated as such.  
> **username_0**: @username_9 the hypothesis came as a result my friend living in San Francisco for a few years and noticing that the weather felt hotter during this festival. He did not look at climate records. Do climate scientists ever conduct any analyses that are not post hoc in this regard? It would take 50 years to test a single hypothesis. Further, the data in the model is primarily data from outside of the period of his anecdotal observation. Would you still consider that data dredging? And would you still say that if I fit the model with data before he lived in the city and it was significant?  
> **username_9**: "Noticing" is the classic example of a *post hoc* hypothesis.  You're allowed to notice: indeed, good scientists notice interesting things all the time.  What is invalid, though, is to claim that the very data or experiences that led to a hypothesis formally verify it.  This principle is enshrined in the requirement that research be *reproducible.*  The onus is not on you to refute your friend's hypothesis; the burden is on your friend to demonstrate reproducibility.  
> **username_0**: @username_9 true.... unfortunately my friend is not concerned with bothersome things such as "evidence" and "statistics"  

---

## 2 answers

### Answer by username_10 on 2019-04-26. Score: 2 | Accepted answer

Let's have a closer look at the p-values in your analysis. By far the most significant effect in your model is the one captured by "groupold", which indicates a significant difference between pre- and post-1972 temperatures. This points to the climate change effect, as noted in username_7's answer.

The next most significant factor we have is "countdown", which indicates, not totally surprisingly, that temperatures tend to increase in the end of June and cool off into July.

Finally, we have the interaction term, which is orders of magnitude less significant than the other factors. The most reasonable explanation for this is that climate change effects are most notable in the extremes of weather. As climate change raises the overall average temperature, it also pushes the extremes higher and lower. Climate change has a significant effect on temperatures, but that effect is even more pronounced in the height of summer.

So no, the festival organizers haven't been using their WeatherSetter 9000 to make the last week of June warmer than average. Rather, climate change has made weeks since 1972 warmer than average, seasonal cycles have always made the last week of June warmer than average, and the climate change effect is especially strong at the extremes of weather.

> **Comments:**
> **username_0**: Brilliant! This is absolutely the most plausible response, in my view, that I have received on this question. Thank you for satisfying my curiosity after 10 months of wondering 8-)  
> **username_10**: @username_0 It would be interesting to test this hypothesis for other locations/times of year, to see if the interaction between climate change effects and seasonal temperature cycles remains consistent. If my supposition is correct, we should see this effect occur in other locations, but be less pronounced during months of less extreme weather.  

---

### Answer by username_7 on 2019-04-24. Score: 5

**You are asking the wrong question**. The real question is why some podunk single city festival and not other larger more costly events, like space launches, national holidays, military campaigns, hurricanes, or a million other festivals. The conspiracy theorists likely picked that festival through data dredging, which means you are not going to disprove it by looking at the same data. If you compare enough festivals you will find some in which the weather does weird things, it is the law of large numbers in action.

**A festival that started in the 1970's is perfect for data dredging, there is a change in the underlying forces of weather in the 1970's.** The undeniable warming trend in global climate starts around the 1970's so if you divide your data sets before and after any point around that time the data will always show a significant difference, unless you correct for that shift. Without correcting for it all you are detecting is climate change, a known factor,n anthropomorphic factor at that. So really humans are altering the weather, just not in the way your friend thinks.

[![enter image description here](https://i.sstatic.net/WKwsi.jpg)](https://i.sstatic.net/WKwsi.jpg)

[![enter image description here](https://i.sstatic.net/LVe7C.png)](https://i.sstatic.net/LVe7C.png)

> **Comments:**
> **username_11**: The question was asked specifically about the last weekend of June, temperature rising and falling. This is unrelated to the long-term trend.  
> **username_7**: but you are noting how this changes over many years, you note the behavior of the weather changes in 1972 that is the significant factor. the fact the average temprature is rising will impact this. temprature rising and falling from day to day is completely normal.  
> **username_7**: The long term trend is controlling the only significant factor, that the data changes behavior in the 1970's nothing else in noteworthy. the rise and fall is just statistical noise, it's not significant. it is pure data dredging. like drawing the bullseye around a cluster of shots already fired and declaring superb accuracy.  
> **username_0**: There was no data dredging. I tested one hypothesis. Every decision I made was strictly driven by theory (misguided theory nonetheless, but still not data dredging). You make it sound like I was testing all different years to split arbitrarily. And secondly, the day-to-day rise and fall is not just statistical noise--that's precisely the point of the post and the slope was non-zero in the "conspiracy-ridden years" and 0 in the "innocuous years". If it were noise, it would cancel out to be a slope of 0 in all of the years.  
> **username_0**: And in regard to your first point, obviously this is a spurious effect. I am just trying to determine why I found it. This is not a common theory that conspiracy theorists found by data dredging. Weather manipulation in general is a known theory, but in regard to this festival this was just something that my friend with absolutely no data analysis skills came up with based on his anecdotal experience and memory.  
> **username_7**: The data dredging was not on your part, but by the people who picked that festival to focus on. if someone examines enough festivals they will find some with odd looking behavior by pure chance. You are misunderstanding what i mean by noise the day to day variation is noise, the change in the variation across the entire time scale is do to climate change, the underlying trend changes in the 1970's. Even if there were no dredging the change in yearly behavior is completely explained by this.  

---

