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However, he was unclear what Sweden should have done differently and at a press conference later on Wednesday later he underlined that "we basically still think that is the right strategy for Sweden".
although he emphasised that "does not disqualify our strategy as a whole".
However, he was unclear what Sweden should have done differently and at a press conference later on Wednesday later he underlined that "we basically still think that is the right strategy for Sweden".
how can he defend the excessive number dead Swedes compared to neighboring countries that went full lockdown and social distancing?
He can't. He's simply defending his negligent manslaughter / reckless endangerment bull
Didn't say it was a conspiracy. Please don't lie. I said you can stand in the same corner as the morons that argue that we didn't land in the moon. They can't back their statements up either, and dodge questions dishonestly, just like you do.
Why do you have to lie to make your case?
Swedens death all cause is the second lowest in 5 years during flu season. Compare Sweden to Belgium and come back.
You were wrong. And you dont understand how lag impacts trendline. Not intelligent of you
Not source data. Still. It's like you dont' have any.
You still suck at this.
Understanding lag is the only data source you need.
Not source data. Still. It's like you dont' have any.
You still suck at this.
Or
Do you believe that daily cases and fatalities only occurred in the last 24 hrs?
No, I do not. That is silly.
Seems I do understand after all. Kind of contradicts your statement right there.
hmmm so how does that data reported effect the trend and the perception of infections?
What data, specifically? Need to understand what you mean to answer question meaningfully.
the daily cases and the daily fatalities. If they are not reported on the actual date of death or the actual onset of infection what do you think that does to the trend and the perception of infection or fatalties?
Discussion, and reasoning over chest thumping. Thank you.
"Daily cases" become data points that indicate "the trend". Individually they may or may not be 100% accurate, but such lags, when taken as larger populations get smoothed out in the overall data. I see this all the time when auditing accounting records and financial statements. A "snapshot" balance sheet has within it the baked in inaccuracy of a bookkeeping lag, i.e. the time between a transaction and the time that transaction is recorded. This aspect is similar to reported losses to insurance companies. People who deal with that data know there is a reporting lag. This lag is accounted for and often estimated to adjust reserves accordingly to get a more accurate reflection of true loss position. So yeah, I understand the concept of lags in reporting.
As for what it does to "perception" that is a much broader question, which would require you to expand it a bit, as I am not entirely sure I understand what you mean.
Your turn:
Is it possible for data to be useful even with baked in inaccuracies? Yes or no.
or if you prefer:
Can you draw meaningful conclusions from data if you know it is in some way inaccurate, if you have a reasonable idea on how it is inaccurate?
No, there is no reasonable conclusion to be made utilizing inaccurate data. Could you imagine having an experiment peer reviewed and published using inaccurate data? Nope. Neither can anyone else who understands science.
The perception is, using lagged data, extends the "pandemic" and increases fear. Not to mention it distorts the trend and makes it less steep. Again you don't get any accurate trends out of using unreliable and invalid data. The cdc even knows this, why they try to sell you on the trendline.
Wrong. I gave two concrete examples of real world ways in which inaccurate data can be used to draw meaningful conclusions.
Financial balance sheets.
Insurance losses.
So the provable answer is "yes, it is possible to draw reasonable conclusions based on inaccurate data". All financial balancesheets of almost every company everywhere includes within them such inaccuracies, and hundreds of trillions of dollars of financial transactions are still made based on that inaccurate data.
Further your statement regarding peer reviewed science is wrong. If data is predictably inaccurate, you can adjust for that, and get a meaningful data set. (edit)A good example is when you use an instrument with a known distortion, such as a tared scale for weight.-RG (/edit)
I see this all the time when I read studies and drill down into the underlying datasets.
You may benefit from a bit of reading here.
Predictably inaccurate: The prevalence and perils of bad big data
https://www2.deloitte.com/us/en/insi...a-quality.html
The very existence of confidence intervals in statistics demonstrates that inaccuracy is baked into the scientific process.
Beware of sweeping statements.
No you said that the lag gets smoothed out over time. Thats got nothing to do with making or continuing policy by using daily inaccuracies. Try again
lol see it all the time in published science. No. You see limitations based on irbs decision to exclude irrelevant data. Again this is not an insurance scam.
By smoothing out I mean:
If you have a bookkeeping lag that you can reasonably estimate to be, say 30 days, and you do monthly balance sheets, which are snapshots in time, for five years, you can know that the underlying trend is valid. the 30 days of lagging transaction for month 1 are included in month 2's sheet, and so on.
Over those 60 periods the results of that 30 day lag becomes smaller and smaller relative to the larger dataset, which is the collection of balance sheets.
With enough data, you can estimate how much that lag effects your financials. "based on five years of data our 30 days lag will look like X dollars in these line items". Large variances from averages are uncommon, so you can estimate based on past experience what those known inaccuracies are, and be expected to be within a small margin most of the time.
This is the "confidence interval" for statistics by the way.
That is what I mean about "smoothed out" over time. Lags, once they can be measured and known, can be factored in.
Sounds familiar.
What are the time periods reflected by the bars in that graph? i.e. how long of a time period does that data represent and when is the cutoff?
(ignoring the obvious typo in his X axis labels)-RG
Last edited by RandomGuy; 06-03-2020 at 02:16 PM.
Indeed.It became apparent during the thread he was cherrypicking and making up sources.
Eventually when i pushed him on sources he blocked me.
Critical thinking time. Hint: http://www.whatweekisit.org/
Last edited by RandomGuy; 06-03-2020 at 02:14 PM.
..
Last edited by RandomGuy; 06-03-2020 at 02:14 PM. Reason: double post
Lol large variances from the average. You can't get a daily average from this. You can only get a reported average lol. Swing and a miss.....
Finland doesn't count ltc facility deaths.... lol just like i said shouldnt count... and they only count deaths to covid that are positives and die from a complication of covid.... a majority of swedens deaths came from ltc
Remember when your argument saying ltc deaths should be counted... why doesn't finland believe you?
Hey, someone finally gave you an answer.
Is your claim Finland's deaths would be on par with Sweden's if they counted those?
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