Discover and read the best of Twitter Threads about #statistical

Most recents (6)

1/ Excited to share how T cell therapies kill #leukemia!! multi-omics + new #computational #singlecell tools for longitudinal analysis πŸ‘‰unexpected answer! cell.com/cell-reports/f…

*πŸ‘* @elhamazizi! πŸ™ @dpeer Cathy Wu @MDAndersonNews @CPRITTexas @ColumbiaBME @sloan_kettering
2/ We studied donor lymphocyte infusion (DLI) - an #Immunotherapy for relapsed #leukemia after #BMT & the #og of #celltherapy. Previously, we showed DLI reversed T cell exhaustion - but didn't know why/how/which T cells were responsible...
ashpublications.org/blood/article/…
3/ To address these ?'s, we modeled intraleukemic T cell dynamics by integrating longitudinal, multimodal data from ~100K T cells (!) during response (R) or resistance (NR: nonresponder) to DLI.
Read 18 tweets
This #meme shows an 'experiment' on #GMO 🌽.

It went #viral multiple times in the last decade.

Do we see a scientific study by 'students in England'❓

I found who took these pictures. How? πŸ‘‡πŸ‘‡πŸ‘‡ 1/..

#OSINT πŸ”Ž
#GeoLocation 🌎
#Verification πŸ“Έ
#SpeurJeMee? 🧐 #HowToOSINT
We'll look for the original context 1⃣ AND we'll examine whether this 'experiment' meets the scientific standard 2⃣. 2/...
1⃣ To find the original context, we need to find who posted this picture first.

You always start this kind of investiagion with muliptle (!) Reverse Image Searches. Always use #Google, #Yandex, #TinEye (πŸ‘‡) and #Bing. 3/...
Read 26 tweets
1/

@US_FDA approved the #Qcollar jugular compression device to "protect athletes' brains during head impacts."

This decision was based on VERY FLAWED DATA and does NOT demonstrate the safety or effectiveness of the Q-collar.

Read detailed thread⬇️

bit.ly/3q9ZZJL
2/

Here is the text from the @FDADeviceInfo press release.

If you don't understand the highly nuanced #MRI technique known as DTI, these results sound straightforward and convincing.

THEY ARE NOT. Don't be fooled by these numbers!πŸ€”

I will dissect these in the thread below. Image
3a/

"No significant changes" is based AVERAGE response, not individual.

First, an easy-to-understand analogy below.

If half the sample experiences an increase and half experiences a decrease, they can cancel each other out to falsely suggest "no change" when one does exists! Image
Read 24 tweets
If the connotation of risk is an intertwined concept and is difficult to quantify, how does a Risk Officer look at it?
Is there any way other than using copula models to determine systemic risk with long tails or a black swan event?
@CQFInstitute @GARP_Risk @SOActuaries
I guess we are worried about Market and Credit Risks or other interrelated financial risks which can create conjoint loss given events.
Any #Gaussian distribution model will enable you to model and predict potential Operational, Liquidity and Balance sheet AL - (Asset - liability) Mismatch, Market and Credit drove losses under normal market conditions.
Read 32 tweets
I am into risk management.
Most of the risk managers are now required to have an advance background in operating technological applications such online trading and price data terminals (Bloomberg/Reuters, etc),
FINTECH, Crypto Assets, Digital Marketing based vendor systems, DLT(Distributional Ledger Technologies) - Blockchain, AI / ALGO based trading in financial markets, Derivatives and Risk Pricing Engines and other Software Computational Programs,
Risk MIS/ERP Project Management Tasks, 4GL Fourth Generation Languages, Data Warehousing, BI, MI, SQL, NoSQL, and so on etc.
Read 6 tweets
A Thread about the best test batsmen 1/25
#India #Aus #NZ #Stats #Cricket

A #statistical breakdown

Kane Williamson: 7115 runs @ 54.31, 24x100s
Virat Kohli: 7318 runs @ 53.41, 27x100s
Steve Smith: 7368 runs @ 61.91, 27x100s

Smith here has the best average by a fair way.
But the joy of cricket is in its intricacies so let’s dive a little deeper. At home the 3 batsmen are absolute run machines.

Kane Williamson: 3788 runs @ 65.31 13x100s
Virat Kohli: 3558 runs @ 68.42 13x100s
Steve Smith: 3485 runs @ 68.33 14x100s
Here their statistics are very similar.

However it is worth noting how hard it is to bat in each of these countries. These figures reflect the average runs of each players team mates in the last 10 years.

Australia: 44.27 runs/wicket
New Zealand: 42.28
India: 44.17
Read 25 tweets

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