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1000 results for “whoosh”
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#kDrama
Five Enough
(2016 - 54 1:10 eps)A widower & 2 kids, living with his In-laws meets a divorced (husband cheated) co-worker with 3 kids & a grandmother. His other 2 siblings also have difficulties in their dating lives. When all the families get involved it's a big mess.
It's like 4 in 1, even at 54 episodes, it flies by. The acting is good, although Shin Hye Sun's character is quite irritating at times. Most of the other characters become quite lovable.
A bit predictable, but the main couple is so good - showing rational behavior & good communication, even with a crazy family. Their elders, drove me crazy, but it was fun watching the adult kids deal with them.
Interesting to see how stigmatized situations - 1 parent households, infidelity & family interconnections were handled.
https://mydramalist.com/16220-five-enough
#FiveEnough #아이가다섯 #AhnJaeWook #안재욱 #SoYooJin #소유진 #SungHoon #방인규 #성훈 #ShinHyeSun #신혜선 #ShimHyungTak #심형탁 #ShimYiYoung #심이영 #AhnWooYeon #안우연 #ImSooHyang #임수향 #WhooshReview
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*** Even if This Love Disappears Tonight (2025) ***
(Netflix 1:46 movie)[!!CW!! Synopsis & non-spoilery review.]
Netflix advertises this movie as "bittersweet." 2 high schoolers, 1 with a disease that resets her memory every day, & 1 with a weak heart, start dating. I watched for the up-and-comer, Choo Young Woo, & ended up loving the FL, Shin Shi Ah, too.
You must suspend disbelief a bit because someone with a weak heart who hates to sweat could not possibly have arms like that.
The acting was worth watching. The story was well-written if contrived. The health problems create some unique situations that make you evaluate what you think about love & memories.
https://mydramalist.com/790594-even-if-this-love-disappears-from-the-world-tonight
#EvenIfThisLoveDisappearsTonight #오늘밤세계에서이사랑이사라진다해도 #ChooYoungWoo #추영우 #ShinShiAh #신시아
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*** Even if This Love Disappears Tonight (2025) ***
(Netflix 1:46 movie)[!!CW!! Synopsis & non-spoilery review.]
Netflix advertises this movie as "bittersweet." 2 high schoolers, 1 with a disease that resets her memory every day, & 1 with a weak heart, start dating. I watched for the up-and-comer, Choo Young Woo, & ended up loving the FL, Shin Shi Ah, too.
You must suspend disbelief a bit because someone with a weak heart who hates to sweat could not possibly have arms like that.
The acting was worth watching. The story was well-written if contrived. The health problems create some unique situations that make you evaluate what you think about love & memories.
https://mydramalist.com/790594-even-if-this-love-disappears-from-the-world-tonight
#EvenIfThisLoveDisappearsTonight #오늘밤세계에서이사랑이사라진다해도 #ChooYoungWoo #추영우 #ShinShiAh #신시아
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*** Even if This Love Disappears Tonight (2025) ***
(Netflix 1:46 movie)[!!CW!! Synopsis & non-spoilery review.]
Netflix advertises this movie as "bittersweet." 2 high schoolers, 1 with a disease that resets her memory every day, & 1 with a weak heart, start dating. I watched for the up-and-comer, Choo Young Woo, & ended up loving the FL, Shin Shi Ah, too.
You must suspend disbelief a bit because someone with a weak heart who hates to sweat could not possibly have arms like that.
The acting was worth watching. The story was well-written if contrived. The health problems create some unique situations that make you evaluate what you think about love & memories.
https://mydramalist.com/790594-even-if-this-love-disappears-from-the-world-tonight
#EvenIfThisLoveDisappearsTonight #오늘밤세계에서이사랑이사라진다해도 #ChooYoungWoo #추영우 #ShinShiAh #신시아
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*** Even if This Love Disappears Tonight (2025) ***
(Netflix 1:46 movie)[!!CW!! Synopsis & non-spoilery review.]
Netflix advertises this movie as "bittersweet." 2 high schoolers, 1 with a disease that resets her memory every day, & 1 with a weak heart, start dating. I watched for the up-and-comer, Choo Young Woo, & ended up loving the FL, Shin Shi Ah, too.
You must suspend disbelief a bit because someone with a weak heart who hates to sweat could not possibly have arms like that.
The acting was worth watching. The story was well-written if contrived. The health problems create some unique situations that make you evaluate what you think about love & memories.
https://mydramalist.com/790594-even-if-this-love-disappears-from-the-world-tonight
#EvenIfThisLoveDisappearsTonight #오늘밤세계에서이사랑이사라진다해도 #ChooYoungWoo #추영우 #ShinShiAh #신시아
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*** Even if This Love Disappears Tonight (2025) ***
(Netflix 1:46 movie)[!!CW!! Synopsis & non-spoilery review.]
Netflix advertises this movie as "bittersweet." 2 high schoolers, 1 with a disease that resets her memory every day, & 1 with a weak heart, start dating. I watched for the up-and-comer, Choo Young Woo, & ended up loving the FL, Shin Shi Ah, too.
You must suspend disbelief a bit because someone with a weak heart who hates to sweat could not possibly have arms like that.
The acting was worth watching. The story was well-written if contrived. The health problems create some unique situations that make you evaluate what you think about love & memories.
https://mydramalist.com/790594-even-if-this-love-disappears-from-the-world-tonight
#EvenIfThisLoveDisappearsTonight #오늘밤세계에서이사랑이사라진다해도 #ChooYoungWoo #추영우 #ShinShiAh #신시아
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*** The Trauma Code: Heroes on Call (2025) ***
(Netflix 8 0:51 eps)[!!CW!! Synopsis & non-spoilery review.]
When your country's trauma response isn't working, hire a genius, military-conflict-trained trauma surgeon who won't stand for people getting in the way of treating patients. The profit-driven admins aren't happy about it, but some younger doctors & a nurse see the opportunity to excel in their professions - but they have little idea of what's in store for them.
Ju Ji Hoon won best actor here & totally deserved it. He brings his story to life. The relationships between the younger colleagues & the renegade doctor are developed beautifully with funny, frustrating, & touching moments. Some OTT action & I'm sure some medical calls that aren't exactly proper, but overall an exciting & enjoyable watch.
https://mydramalist.com/54697-golden-hour
#TheTraumaCodeHeroesOnCall #TheTraumaCode #중증외상센터
#JuJiHoon #주지훈 #ChooYoungWoo #추영우 #HaYoung #하영 #YoonKyungHo #윤경호 #JungJaeKwang #정재광 -
*** The Trauma Code: Heroes on Call (2025) ***
(Netflix 8 0:51 eps)[!!CW!! Synopsis & non-spoilery review.]
When your country's trauma response isn't working, hire a genius, military-conflict-trained trauma surgeon who won't stand for people getting in the way of treating patients. The profit-driven admins aren't happy about it, but some younger doctors & a nurse see the opportunity to excel in their professions - but they have little idea of what's in store for them.
Ju Ji Hoon won best actor here & totally deserved it. He brings his story to life. The relationships between the younger colleagues & the renegade doctor are developed beautifully with funny, frustrating, & touching moments. Some OTT action & I'm sure some medical calls that aren't exactly proper, but overall an exciting & enjoyable watch.
https://mydramalist.com/54697-golden-hour
#TheTraumaCodeHeroesOnCall #TheTraumaCode #중증외상센터
#JuJiHoon #주지훈 #ChooYoungWoo #추영우 #HaYoung #하영 #YoonKyungHo #윤경호 #JungJaeKwang #정재광 -
*** The Trauma Code: Heroes on Call (2025) ***
(Netflix 8 0:51 eps)[!!CW!! Synopsis & non-spoilery review.]
When your country's trauma response isn't working, hire a genius, military-conflict-trained trauma surgeon who won't stand for people getting in the way of treating patients. The profit-driven admins aren't happy about it, but some younger doctors & a nurse see the opportunity to excel in their professions - but they have little idea of what's in store for them.
Ju Ji Hoon won best actor here & totally deserved it. He brings his story to life. The relationships between the younger colleagues & the renegade doctor are developed beautifully with funny, frustrating, & touching moments. Some OTT action & I'm sure some medical calls that aren't exactly proper, but overall an exciting & enjoyable watch.
https://mydramalist.com/54697-golden-hour
#TheTraumaCodeHeroesOnCall #TheTraumaCode #중증외상센터
#JuJiHoon #주지훈 #ChooYoungWoo #추영우 #HaYoung #하영 #YoonKyungHo #윤경호 #JungJaeKwang #정재광 -
*** The Trauma Code: Heroes on Call (2025) ***
(Netflix 8 0:51 eps)[!!CW!! Synopsis & non-spoilery review.]
When your country's trauma response isn't working, hire a genius, military-conflict-trained trauma surgeon who won't stand for people getting in the way of treating patients. The profit-driven admins aren't happy about it, but some younger doctors & a nurse see the opportunity to excel in their professions - but they have little idea of what's in store for them.
Ju Ji Hoon won best actor here & totally deserved it. He brings his story to life. The relationships between the younger colleagues & the renegade doctor are developed beautifully with funny, frustrating, & touching moments. Some OTT action & I'm sure some medical calls that aren't exactly proper, but overall an exciting & enjoyable watch.
https://mydramalist.com/54697-golden-hour
#TheTraumaCodeHeroesOnCall #TheTraumaCode #중증외상센터
#JuJiHoon #주지훈 #ChooYoungWoo #추영우 #HaYoung #하영 #YoonKyungHo #윤경호 #JungJaeKwang #정재광 -
*** The Trauma Code: Heroes on Call (2025) ***
(Netflix 8 0:51 eps)[!!CW!! Synopsis & non-spoilery review.]
When your country's trauma response isn't working, hire a genius, military-conflict-trained trauma surgeon who won't stand for people getting in the way of treating patients. The profit-driven admins aren't happy about it, but some younger doctors & a nurse see the opportunity to excel in their professions - but they have little idea of what's in store for them.
Ju Ji Hoon won best actor here & totally deserved it. He brings his story to life. The relationships between the younger colleagues & the renegade doctor are developed beautifully with funny, frustrating, & touching moments. Some OTT action & I'm sure some medical calls that aren't exactly proper, but overall an exciting & enjoyable watch.
https://mydramalist.com/54697-golden-hour
#TheTraumaCodeHeroesOnCall #TheTraumaCode #중증외상센터
#JuJiHoon #주지훈 #ChooYoungWoo #추영우 #HaYoung #하영 #YoonKyungHo #윤경호 #JungJaeKwang #정재광 -
#kDrama
Once Upon a Small Town
(2022 - Netflix, 12 0:34 eps)I had no idea this was so short when I started it. It's a darling, sweet, tropey drama with a love triangle set in the countryside.
The MLs were only 22 when this was filmed, but they carry off the older characters well. There was good chemistry & the FL did a fantastic job of being sweet without being cringe. Even in this short drama, she had good character growth. The child actors are so cute. I've seen the boy in many series, but the girl was unfamiliar & I thought she was fabulous. She'll be one to watch.
This one could become a comfort watch for me - there are even puppies & a bunny!
https://mydramalist.com/705675-country-diary-by-chance
#OnceUponASmallTown #어쩌다전원일기 #Joy #조이
#ChooYoungWoo #추영우 #BaekSungChul #백성철 #WhooshReview -
#kDrama
Once Upon a Small Town
(2022 - Netflix, 12 0:34 eps)I had no idea this was so short when I started it. It's a darling, sweet, tropey drama with a love triangle set in the countryside.
The MLs were only 22 when this was filmed, but they carry off the older characters well. There was good chemistry & the FL did a fantastic job of being sweet without being cringe. Even in this short drama, she had good character growth. The child actors are so cute. I've seen the boy in many series, but the girl was unfamiliar & I thought she was fabulous. She'll be one to watch.
This one could become a comfort watch for me - there are even puppies & a bunny!
https://mydramalist.com/705675-country-diary-by-chance
#OnceUponASmallTown #어쩌다전원일기 #Joy #조이
#ChooYoungWoo #추영우 #BaekSungChul #백성철 #WhooshReview -
#kDrama
Once Upon a Small Town
(2022 - Netflix, 12 0:34 eps)I had no idea this was so short when I started it. It's a darling, sweet, tropey drama with a love triangle set in the countryside.
The MLs were only 22 when this was filmed, but they carry off the older characters well. There was good chemistry & the FL did a fantastic job of being sweet without being cringe. Even in this short drama, she had good character growth. The child actors are so cute. I've seen the boy in many series, but the girl was unfamiliar & I thought she was fabulous. She'll be one to watch.
This one could become a comfort watch for me - there are even puppies & a bunny!
https://mydramalist.com/705675-country-diary-by-chance
#OnceUponASmallTown #어쩌다전원일기 #Joy #조이
#ChooYoungWoo #추영우 #BaekSungChul #백성철 #WhooshReview -
#kDrama
Once Upon a Small Town
(2022 - Netflix, 12 0:34 eps)I had no idea this was so short when I started it. It's a darling, sweet, tropey drama with a love triangle set in the countryside.
The MLs were only 22 when this was filmed, but they carry off the older characters well. There was good chemistry & the FL did a fantastic job of being sweet without being cringe. Even in this short drama, she had good character growth. The child actors are so cute. I've seen the boy in many series, but the girl was unfamiliar & I thought she was fabulous. She'll be one to watch.
This one could become a comfort watch for me - there are even puppies & a bunny!
https://mydramalist.com/705675-country-diary-by-chance
#OnceUponASmallTown #어쩌다전원일기 #Joy #조이
#ChooYoungWoo #추영우 #BaekSungChul #백성철 #WhooshReview -
#ADramaLunies2025
07-Feb : Most memorable character development?Jang Tae Sang (Park Seo Joon) in Gyeongseong Creature. A man who lived for himself begins to question his values as he becomes concerned for someone else. It's difficult for a character to change so much and still have it be believable. Park Seo Joon does an amazing job.
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#ADramaLunies2025
07-Feb : Most memorable character development?Jang Tae Sang (Park Seo Joon) in Gyeongseong Creature. A man who lived for himself begins to question his values as he becomes concerned for someone else. It's difficult for a character to change so much and still have it be believable. Park Seo Joon does an amazing job.
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#ADramaLunies2025
07-Feb : Most memorable character development?Jang Tae Sang (Park Seo Joon) in Gyeongseong Creature. A man who lived for himself begins to question his values as he becomes concerned for someone else. It's difficult for a character to change so much and still have it be believable. Park Seo Joon does an amazing job.
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#ADramaLunies2025
07-Feb : Most memorable character development?Jang Tae Sang (Park Seo Joon) in Gyeongseong Creature. A man who lived for himself begins to question his values as he becomes concerned for someone else. It's difficult for a character to change so much and still have it be believable. Park Seo Joon does an amazing job.
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#kDrama
Fight for My Way (rewatch)
(2017 - Viki, 16 1:00 eps)4 friends in their late 20s with unrealized dreams & boring lives create a good friends-to-lovers story with incredible acting, a strong 2nd couple, & touching friendship.
A great Park Seo Joon role (he may be dressed better in others, but his acting shines here) - he plays a not-bright, vulnerable, desperate, yet talented man. Kim Ji Won is amazing. I also loved Song Ha Yoon's acting here - is she really the same actress as "Marry My Husband"'s villain? What a range.
A few tropes: a dark past, old enemies, & love triangles but since these are average people, it comes off differently. The 2nd couple's story is unexpected. There is romance & great MMA action, but the stars of this drama are young adults coming into their own, creating the lives they want for themselves, despite hardships.
https://mydramalist.com/22472-fight-for-my-way
#FightForMyWay #쌈마이웨이 #ParkSeoJoon #박서준 #KimJiWon #김지원 #AhnJaeHong #안재홍 #SongHaYoon #송하윤 #WhooshReview
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#kDrama
Fight for My Way (rewatch)
(2017 - Viki, 16 1:00 eps)4 friends in their late 20s with unrealized dreams & boring lives create a good friends-to-lovers story with incredible acting, a strong 2nd couple, & touching friendship.
A great Park Seo Joon role (he may be dressed better in others, but his acting shines here) - he plays a not-bright, vulnerable, desperate, yet talented man. Kim Ji Won is amazing. I also loved Song Ha Yoon's acting here - is she really the same actress as "Marry My Husband"'s villain? What a range.
A few tropes: a dark past, old enemies, & love triangles but since these are average people, it comes off differently. The 2nd couple's story is unexpected. There is romance & great MMA action, but the stars of this drama are young adults coming into their own, creating the lives they want for themselves, despite hardships.
https://mydramalist.com/22472-fight-for-my-way
#FightForMyWay #쌈마이웨이 #ParkSeoJoon #박서준 #KimJiWon #김지원 #AhnJaeHong #안재홍 #SongHaYoon #송하윤 #WhooshReview
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#kDrama
Fight for My Way (rewatch)
(2017 - Viki, 16 1:00 eps)4 friends in their late 20s with unrealized dreams & boring lives create a good friends-to-lovers story with incredible acting, a strong 2nd couple, & touching friendship.
A great Park Seo Joon role (he may be dressed better in others, but his acting shines here) - he plays a not-bright, vulnerable, desperate, yet talented man. Kim Ji Won is amazing. I also loved Song Ha Yoon's acting here - is she really the same actress as "Marry My Husband"'s villain? What a range.
A few tropes: a dark past, old enemies, & love triangles but since these are average people, it comes off differently. The 2nd couple's story is unexpected. There is romance & great MMA action, but the stars of this drama are young adults coming into their own, creating the lives they want for themselves, despite hardships.
https://mydramalist.com/22472-fight-for-my-way
#FightForMyWay #쌈마이웨이 #ParkSeoJoon #박서준 #KimJiWon #김지원 #AhnJaeHong #안재홍 #SongHaYoon #송하윤 #WhooshReview
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#kDrama
Fight for My Way (rewatch)
(2017 - Viki, 16 1:00 eps)4 friends in their late 20s with unrealized dreams & boring lives create a good friends-to-lovers story with incredible acting, a strong 2nd couple, & touching friendship.
A great Park Seo Joon role (he may be dressed better in others, but his acting shines here) - he plays a not-bright, vulnerable, desperate, yet talented man. Kim Ji Won is amazing. I also loved Song Ha Yoon's acting here - is she really the same actress as "Marry My Husband"'s villain? What a range.
A few tropes: a dark past, old enemies, & love triangles but since these are average people, it comes off differently. The 2nd couple's story is unexpected. There is romance & great MMA action, but the stars of this drama are young adults coming into their own, creating the lives they want for themselves, despite hardships.
https://mydramalist.com/22472-fight-for-my-way
#FightForMyWay #쌈마이웨이 #ParkSeoJoon #박서준 #KimJiWon #김지원 #AhnJaeHong #안재홍 #SongHaYoon #송하윤 #WhooshReview
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My Road to Bayesian Stats
By 2015, I had heard of Bayesian Stats but didn’t bother to go deeper into it. After all, significance stars, and p-values worked fine. I started to explore Bayesian Statistics when considering small sample sizes in biological experiments. How much can you say when you are comparing means of 6 or even 60 observations? This is the nature work at the edge of knowledge. Not knowing what to expect is normal. Multiple possible routes to a seen a result is normal. Not knowing how to pick the route to the observed result is also normal. Yet, our statistics fails to capture this reality and the associated uncertainties. There must be a way I thought.
Free Curve to the Point: Accompanying Sound of Geometric Curves (1925) print in high resolution by Wassily Kandinsky. Original from The MET Museum. Digitally enhanced by rawpixel.I started by searching for ways to overcome small sample sizes. There are minimum sample sizes recommended for t-tests. Thirty is an often quoted number with qualifiers. Bayesian stats does not have a minimum sample size. This had me intrigued. Surely, this can’t be a thing. But it is. Bayesian stats creates a mathematical model using your observations and then samples from that model to make comparisons. If you have any exposure to AI, you can think of this a bit like training an AI model. Of course the more data you have the better the model can be. But even with a little data we can make progress.
How do you say, there is something happening and it’s interesting, but we are only x% sure. Frequentist stats have no way through. All I knew was to apply the t-test and if there are “***” in the plot, I’m golden. That isn’t accurate though. Low p-values indicate the strength of evidence against the null hypothesis. Let’s take a minute to unpack that. The null hypothesis is that nothing is happening. If you have a control set and do a treatment on the other set, the null hypothesis says that there is no difference. So, a low p-value says that it is unlikely that the null hypothesis is true. But that does not imply that the alternative hypothesis is true. What’s worse is that there is no way for us to say that the control and experiment have no difference. We can’t accept the null hypothesis using p-values either.
Guess what? Bayes stats can do all those things. It can measure differences, accept and reject both null and alternative hypotheses, even communicate how uncertain we are (more on this later). All without making assumptions about our data.
It’s often overlooked, but frequentist analysis also requires the data to have certain properties like normality and equal variance. Biological processes have complex behavior and, unless observed, assuming normality and equal variance is perilous. The danger only goes up with small sample sizes. Again, Bayes requires you to make no assumptions about your data. Whatever shape the distribution is, so called outliers and all, it all goes into the model. Small sample sets do produce weaker fits, but this is kept transparent.
Transparency is one of the key strengths of Bayesian stats. It requires you to work a little bit harder on two fronts though. First you have to think about your data generating process (DGP). This means how do the data points you observe came to be. As we said, the process is often unknown. We have at best some guesses of how this could happen. Thankfully, we have a nice way to represent this. DAGs, directed acyclic graphs, are a fancy name for a simple diagram showing what affects what. Most of the time we are trying to discover the DAG, ie the pathway of a biological outcome. Even if you don’t do Bayesian stats, using DAGs to lay out your thoughts is a great. In Bayesian stats the DAGs can be used to test if your model fits the data we observe. If the DAG captures the data generating process the fit is good, and not if it doesn’t.
The other hard bit is doing analysis and communicating the results. Bayesian stats forces you to be verbose about your assumptions in your model. This part is almost magicked away in t-tests. Frequentist stats also makes assumptions about the model that your data is assumed to follow. It all happens so quickly that there isn’t even a second to think about it. You put in your data, click t-test and woosh! You see stars. In Bayesian stats stating the assumptions you make in your model (using DAGs and hypothesis about DGPs) communicates to the world what and why you think this phenomenon occurs.
Discovering causality is the whole reason for doing science. Knowing the causality allows us to intervene in the forms of treatments and drugs. But if my tools don’t allow me to be transparent and worse if they block people from correcting me, why bother?
Richard McElreath says it best:
There is no method for making causal models other than science. There is no method to science other than honest anarchy.
#AI #BayesianStatistics #BiologicalDataAnalysis #Business #CausalInference #DAGs #DataGeneratingProcess #ExperimentalDesign #FrequentistVsBayesian #Leadership #philosophy #ScientificMethod #SmallSampleSize #StatisticalModeling #StatisticalPhilosophy #TransparentScience #UncertaintyQuantification
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My Road to Bayesian Stats
By 2015, I had heard of Bayesian Stats but didn’t bother to go deeper into it. After all, significance stars, and p-values worked fine. I started to explore Bayesian Statistics when considering small sample sizes in biological experiments. How much can you say when you are comparing means of 6 or even 60 observations? This is the nature work at the edge of knowledge. Not knowing what to expect is normal. Multiple possible routes to a seen a result is normal. Not knowing how to pick the route to the observed result is also normal. Yet, our statistics fails to capture this reality and the associated uncertainties. There must be a way I thought.
Free Curve to the Point: Accompanying Sound of Geometric Curves (1925) print in high resolution by Wassily Kandinsky. Original from The MET Museum. Digitally enhanced by rawpixel.I started by searching for ways to overcome small sample sizes. There are minimum sample sizes recommended for t-tests. Thirty is an often quoted number with qualifiers. Bayesian stats does not have a minimum sample size. This had me intrigued. Surely, this can’t be a thing. But it is. Bayesian stats creates a mathematical model using your observations and then samples from that model to make comparisons. If you have any exposure to AI, you can think of this a bit like training an AI model. Of course the more data you have the better the model can be. But even with a little data we can make progress.
How do you say, there is something happening and it’s interesting, but we are only x% sure. Frequentist stats have no way through. All I knew was to apply the t-test and if there are “***” in the plot, I’m golden. That isn’t accurate though. Low p-values indicate the strength of evidence against the null hypothesis. Let’s take a minute to unpack that. The null hypothesis is that nothing is happening. If you have a control set and do a treatment on the other set, the null hypothesis says that there is no difference. So, a low p-value says that it is unlikely that the null hypothesis is true. But that does not imply that the alternative hypothesis is true. What’s worse is that there is no way for us to say that the control and experiment have no difference. We can’t accept the null hypothesis using p-values either.
Guess what? Bayes stats can do all those things. It can measure differences, accept and reject both null and alternative hypotheses, even communicate how uncertain we are (more on this later). All without making assumptions about our data.
It’s often overlooked, but frequentist analysis also requires the data to have certain properties like normality and equal variance. Biological processes have complex behavior and, unless observed, assuming normality and equal variance is perilous. The danger only goes up with small sample sizes. Again, Bayes requires you to make no assumptions about your data. Whatever shape the distribution is, so called outliers and all, it all goes into the model. Small sample sets do produce weaker fits, but this is kept transparent.
Transparency is one of the key strengths of Bayesian stats. It requires you to work a little bit harder on two fronts though. First you have to think about your data generating process (DGP). This means how do the data points you observe came to be. As we said, the process is often unknown. We have at best some guesses of how this could happen. Thankfully, we have a nice way to represent this. DAGs, directed acyclic graphs, are a fancy name for a simple diagram showing what affects what. Most of the time we are trying to discover the DAG, ie the pathway of a biological outcome. Even if you don’t do Bayesian stats, using DAGs to lay out your thoughts is a great. In Bayesian stats the DAGs can be used to test if your model fits the data we observe. If the DAG captures the data generating process the fit is good, and not if it doesn’t.
The other hard bit is doing analysis and communicating the results. Bayesian stats forces you to be verbose about your assumptions in your model. This part is almost magicked away in t-tests. Frequentist stats also makes assumptions about the model that your data is assumed to follow. It all happens so quickly that there isn’t even a second to think about it. You put in your data, click t-test and woosh! You see stars. In Bayesian stats stating the assumptions you make in your model (using DAGs and hypothesis about DGPs) communicates to the world what and why you think this phenomenon occurs.
Discovering causality is the whole reason for doing science. Knowing the causality allows us to intervene in the forms of treatments and drugs. But if my tools don’t allow me to be transparent and worse if they block people from correcting me, why bother?
Richard McElreath says it best:
There is no method for making causal models other than science. There is no method to science other than honest anarchy.
#AI #BayesianStatistics #BiologicalDataAnalysis #Business #CausalInference #DAGs #DataGeneratingProcess #ExperimentalDesign #FrequentistVsBayesian #Leadership #philosophy #ScientificMethod #SmallSampleSize #StatisticalModeling #StatisticalPhilosophy #TransparentScience #UncertaintyQuantification
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My Road to Bayesian Stats
By 2015, I had heard of Bayesian Stats but didn’t bother to go deeper into it. After all, significance stars, and p-values worked fine. I started to explore Bayesian Statistics when considering small sample sizes in biological experiments. How much can you say when you are comparing means of 6 or even 60 observations? This is the nature work at the edge of knowledge. Not knowing what to expect is normal. Multiple possible routes to a seen a result is normal. Not knowing how to pick the route to the observed result is also normal. Yet, our statistics fails to capture this reality and the associated uncertainties. There must be a way I thought.
Free Curve to the Point: Accompanying Sound of Geometric Curves (1925) print in high resolution by Wassily Kandinsky. Original from The MET Museum. Digitally enhanced by rawpixel.I started by searching for ways to overcome small sample sizes. There are minimum sample sizes recommended for t-tests. Thirty is an often quoted number with qualifiers. Bayesian stats does not have a minimum sample size. This had me intrigued. Surely, this can’t be a thing. But it is. Bayesian stats creates a mathematical model using your observations and then samples from that model to make comparisons. If you have any exposure to AI, you can think of this a bit like training an AI model. Of course the more data you have the better the model can be. But even with a little data we can make progress.
How do you say, there is something happening and it’s interesting, but we are only x% sure. Frequentist stats have no way through. All I knew was to apply the t-test and if there are “***” in the plot, I’m golden. That isn’t accurate though. Low p-values indicate the strength of evidence against the null hypothesis. Let’s take a minute to unpack that. The null hypothesis is that nothing is happening. If you have a control set and do a treatment on the other set, the null hypothesis says that there is no difference. So, a low p-value says that it is unlikely that the null hypothesis is true. But that does not imply that the alternative hypothesis is true. What’s worse is that there is no way for us to say that the control and experiment have no difference. We can’t accept the null hypothesis using p-values either.
Guess what? Bayes stats can do all those things. It can measure differences, accept and reject both null and alternative hypotheses, even communicate how uncertain we are (more on this later). All without making assumptions about our data.
It’s often overlooked, but frequentist analysis also requires the data to have certain properties like normality and equal variance. Biological processes have complex behavior and, unless observed, assuming normality and equal variance is perilous. The danger only goes up with small sample sizes. Again, Bayes requires you to make no assumptions about your data. Whatever shape the distribution is, so called outliers and all, it all goes into the model. Small sample sets do produce weaker fits, but this is kept transparent.
Transparency is one of the key strengths of Bayesian stats. It requires you to work a little bit harder on two fronts though. First you have to think about your data generating process (DGP). This means how do the data points you observe came to be. As we said, the process is often unknown. We have at best some guesses of how this could happen. Thankfully, we have a nice way to represent this. DAGs, directed acyclic graphs, are a fancy name for a simple diagram showing what affects what. Most of the time we are trying to discover the DAG, ie the pathway of a biological outcome. Even if you don’t do Bayesian stats, using DAGs to lay out your thoughts is a great. In Bayesian stats the DAGs can be used to test if your model fits the data we observe. If the DAG captures the data generating process the fit is good, and not if it doesn’t.
The other hard bit is doing analysis and communicating the results. Bayesian stats forces you to be verbose about your assumptions in your model. This part is almost magicked away in t-tests. Frequentist stats also makes assumptions about the model that your data is assumed to follow. It all happens so quickly that there isn’t even a second to think about it. You put in your data, click t-test and woosh! You see stars. In Bayesian stats stating the assumptions you make in your model (using DAGs and hypothesis about DGPs) communicates to the world what and why you think this phenomenon occurs.
Discovering causality is the whole reason for doing science. Knowing the causality allows us to intervene in the forms of treatments and drugs. But if my tools don’t allow me to be transparent and worse if they block people from correcting me, why bother?
Richard McElreath says it best:
There is no method for making causal models other than science. There is no method to science other than honest anarchy.
#AI #BayesianStatistics #BiologicalDataAnalysis #Business #CausalInference #DAGs #DataGeneratingProcess #ExperimentalDesign #FrequentistVsBayesian #Leadership #philosophy #ScientificMethod #SmallSampleSize #StatisticalModeling #StatisticalPhilosophy #TransparentScience #UncertaintyQuantification
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My Road to Bayesian Stats
By 2015, I had heard of Bayesian Stats but didn’t bother to go deeper into it. After all, significance stars, and p-values worked fine. I started to explore Bayesian Statistics when considering small sample sizes in biological experiments. How much can you say when you are comparing means of 6 or even 60 observations? This is the nature work at the edge of knowledge. Not knowing what to expect is normal. Multiple possible routes to a seen a result is normal. Not knowing how to pick the route to the observed result is also normal. Yet, our statistics fails to capture this reality and the associated uncertainties. There must be a way I thought.
Free Curve to the Point: Accompanying Sound of Geometric Curves (1925) print in high resolution by Wassily Kandinsky. Original from The MET Museum. Digitally enhanced by rawpixel.I started by searching for ways to overcome small sample sizes. There are minimum sample sizes recommended for t-tests. Thirty is an often quoted number with qualifiers. Bayesian stats does not have a minimum sample size. This had me intrigued. Surely, this can’t be a thing. But it is. Bayesian stats creates a mathematical model using your observations and then samples from that model to make comparisons. If you have any exposure to AI, you can think of this a bit like training an AI model. Of course the more data you have the better the model can be. But even with a little data we can make progress.
How do you say, there is something happening and it’s interesting, but we are only x% sure. Frequentist stats have no way through. All I knew was to apply the t-test and if there are “***” in the plot, I’m golden. That isn’t accurate though. Low p-values indicate the strength of evidence against the null hypothesis. Let’s take a minute to unpack that. The null hypothesis is that nothing is happening. If you have a control set and do a treatment on the other set, the null hypothesis says that there is no difference. So, a low p-value says that it is unlikely that the null hypothesis is true. But that does not imply that the alternative hypothesis is true. What’s worse is that there is no way for us to say that the control and experiment have no difference. We can’t accept the null hypothesis using p-values either.
Guess what? Bayes stats can do all those things. It can measure differences, accept and reject both null and alternative hypotheses, even communicate how uncertain we are (more on this later). All without making assumptions about our data.
It’s often overlooked, but frequentist analysis also requires the data to have certain properties like normality and equal variance. Biological processes have complex behavior and, unless observed, assuming normality and equal variance is perilous. The danger only goes up with small sample sizes. Again, Bayes requires you to make no assumptions about your data. Whatever shape the distribution is, so called outliers and all, it all goes into the model. Small sample sets do produce weaker fits, but this is kept transparent.
Transparency is one of the key strengths of Bayesian stats. It requires you to work a little bit harder on two fronts though. First you have to think about your data generating process (DGP). This means how do the data points you observe came to be. As we said, the process is often unknown. We have at best some guesses of how this could happen. Thankfully, we have a nice way to represent this. DAGs, directed acyclic graphs, are a fancy name for a simple diagram showing what affects what. Most of the time we are trying to discover the DAG, ie the pathway of a biological outcome. Even if you don’t do Bayesian stats, using DAGs to lay out your thoughts is a great. In Bayesian stats the DAGs can be used to test if your model fits the data we observe. If the DAG captures the data generating process the fit is good, and not if it doesn’t.
The other hard bit is doing analysis and communicating the results. Bayesian stats forces you to be verbose about your assumptions in your model. This part is almost magicked away in t-tests. Frequentist stats also makes assumptions about the model that your data is assumed to follow. It all happens so quickly that there isn’t even a second to think about it. You put in your data, click t-test and woosh! You see stars. In Bayesian stats stating the assumptions you make in your model (using DAGs and hypothesis about DGPs) communicates to the world what and why you think this phenomenon occurs.
Discovering causality is the whole reason for doing science. Knowing the causality allows us to intervene in the forms of treatments and drugs. But if my tools don’t allow me to be transparent and worse if they block people from correcting me, why bother?
Richard McElreath says it best:
There is no method for making causal models other than science. There is no method to science other than honest anarchy.
#AI #BayesianStatistics #BiologicalDataAnalysis #Business #CausalInference #DAGs #DataGeneratingProcess #ExperimentalDesign #FrequentistVsBayesian #Leadership #philosophy #ScientificMethod #SmallSampleSize #StatisticalModeling #StatisticalPhilosophy #TransparentScience #UncertaintyQuantification
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Still half-asleep, Jack was shuffling towards the kitchen when he was startled by a noise coming from his small utility room.
"What the ...", he began to yell as he wrenched open the door. His eyes went wide.
There, before him, stood a menacing-looking humanoid robot, faceless, bright white, and littered in tiny perforations.
Jack flinched as the robot's arm whipped out and snatched something from next to him. A blur of rapid arm movements and the robot was wearing Jack's favourite shirt. The robot appeared to breath-in, expanding not only it's torso but it's arms too, and then let off a whoosh of steam.
Another blur of robot arms and the shirt was folded and being offered to Jack.
"Your shirt is ready to wear", said a voice as Jack sheepishly recalled his latest impulse purchase.
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@cinderdazzle612 He really does - even in the middle of being cursed and trying to solve a mystery. He was perfect for this role!
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@commonst @jodmentum @hanktank61
"When the Weather Is Fine" & "The Third Charm" are my slow, savory, insightful writing gems. I won't count "Summer Strike" - it's almost there, but the criminal activity puts it in a different genre for me (not bad, actually really good, but just a bit too much suspense for a savory watch).
Note: 3rd Charm has a horrible MDL rating & you have to get past the 1st few episodes before it becomes solid.
#kDrama #WhenTheWeatherIsFine #날씨가 좋으면 찾아가겠어요 #TheThirdCharm #제3의매력