Why I Look at Data Differently

Emily Oster

11 min Read Emily Oster

Emily Oster

A question I get frequently: Why does my analysis often disagree with groups like the American Academy of Pediatrics or other national bodies, or other public health experts, or Andrew Huberman (lately I get that last one a lot)? The particular context is often in observational studies of topics in nutrition or development.

Some examples:

The recent analysis of processed food and cancer is emblematic of many of these cases. In that post, I argued that the relationship observed in the data was extremely likely to reflect correlation and not causation. My argument rested on the observation that people who ate differently also differed on many other features.

In response, a reader wrote in this question:

You emphasize causation vs. correlation, and I think you are pointing to potential confounders that could actually be the root cause of the findings. My question is — can’t and don’t study researchers control for that in their analysis? Can’t they look at the link between screen time and academic success while keeping potential confounders equal across the comparison groups? And if so, wouldn’t that help rule out the impact of other factors and strengthen the case that there is a true link?

This is a very good question, and it clarifies for me where many of the disagreements lie.

The questioner essentially notes: the reason we know that the processed food groups differ a lot is that the authors can see the characteristics of individuals. But because they see these characteristics, they can adjust for them (using statistical tools). While it’s true that education levels are higher among those who eat less processed food, by adjusting for education we can come closer to comparing people with the same education level who eat different kinds of food.

However, in typical data you cannot observe and adjust for all differences. You do not see everything about people. Sometimes this is simply because our variables are rough: we see whether someone has a family income above or below the poverty line, but not any more details, and those details are important. There are also characteristics we almost never capture in data, like How much do you like exercise? or How healthy are your partner’s behaviors? or even Where is the closest farmers’ market? 

For both of these reasons, in nearly all examples, we worry about residual confounding. That’s the concern that there are still other important differences across groups that might drive the results. Most papers list this possibility in their “limitations” section.

We all agree that this is a concern. Where we differ is in how much of a limitation we believe it to be. In my view, in these contexts (and in many others), residual confounding is so significant a factor that it is hopeless to try to learn causality from this type of observational data. 

This position drives a lot of my concerns with existing research. Thinking about these issues is a huge part of my research and teaching. So I thought I’d spend a little time today explaining why I hold this position. I’m going to start with theory and then discuss two pieces of evidence.

A quick note: this post focuses on concerns about approaches which take non-randomized data and argue for causality based on including observed controls. There are other approaches to non-randomized data (i.e. difference-in-difference, event studies) which have stronger causality claims. See some discussion of those in this older post.

Theory

Conceptually, the gold standard for causality is a randomized controlled trial. In the canonical version of such a trial, researchers randomly allocate half of their participants to treatment and half to control. They then follow them over time and compare outcomes. The key is that because you randomly choose who is in the treatment group, you expect them, on average, to be the same as the control other than the presence of the treatment. So you can get a causal effect of treatment by comparing the groups.

Randomized trials are great but not always possible. A lot of what is done in public health and economics aims to estimate causal effects without randomized trials. The key to doing this is to isolate a source of randomness in some treatment, even if that randomization is not explicit.

For example: Imagine that you’re interested in the effect of going to a selective high school on college enrollment. One simple thing to do would be to compare the students who went to the selective high school with those who did not. But this would be tricky, because there are so many other differences across the students.

Now imagine that the way that admission to the high school works is based on a test score: if you get a score above some cutoff, you get in, and if you are below, you do not. With that kind of mechanism, we can get closer to causality. Let’s say the cutoff score is 150. You’ve got some students who scored 149 and some who scored 150. The second group gets in, the first doesn’t. But their scores are really similar. It may be reasonable to claim that it is effectively random whether you got 149 or 150 — the difference is so small, it could happen by chance. In that case, you can try to figure out the causal effect of the selective high school by comparing the students just above the cutoff with those just below.

This particular technique is called regression discontinuity; it’s part of a suite of approaches to estimate causal effects that take advantage of these moments of randomness in the world. The moments do not need to be truly random, but they do need to be driving the treatment and not driving the outcome you are interested in.

We can take this lens to the kind of observational data that we often consider. Let’s return to the processed food and cancer example. The approach in that paper was to compare people who ate a lot of processed food with those who ate less. Clearly, in raw terms, this would be unacceptable because there are huge differences across those groups. The authors argue, though, that once they control for those differences, they have mostly addressed this issue.

This argument comes down to: once I control for the variables I see, the choice about processed food is effectively random, or at least unrelated to other aspects of health.

I find this fundamentally unpalatable. Take two people who have the same level of income, the same education, and the same preexisting conditions, and one of them eats a lot of processed food and the other eats a lot of whole grains and fresh vegetables. I contend that those people are still different. That their choice of food isn’t effectively random — it’s related to other things about them, things we cannot see. Adding more and more controls doesn’t necessarily make this problem better. You’re isolating smaller and smaller groups, but still you have to ask why people are making different food choices.

Food is a huge part of our lives, and our choices about it are not especially random. Sure, it may be random whether I have a sandwich or a salad for lunch today, but whether I’m eating a bag of Cheetos or a tomato and avocado on whole-grain toast — that is simply not random and not unrelated to other health choices.

This is where, perhaps, I conceptually differ from others. I have to imagine that researchers doing this work do not hold this view. It must be that they think that once we adjust for the observed controls, the differences across people are random, or at least are unrelated to other elements of their health.

This is a theoretical disagreement. But there are at least two things in data that have really reinforced my view — one from my own research and one example from my books.

Selection on observables: Vitamins

Underlying the issue of correlation versus causation are human choices. This is especially true in nutrition. The reason it is hard to learn about causality is that different people make different choices. One of the possible reasons for those different choices is different information, or different processing of information.

A few years ago, I got curious about the role of information — of news — in driving these choices, and I wrote a paper that looked at what happened to health behaviors after changes in health information. I wrote at more length about that paper here, but the basic idea was to analyze who adopts new health behaviors when news comes out suggesting those behaviors are good.

The main application is vitamin E. In the early 1990s, a study came out suggesting vitamin E supplements improved health. What happened as a result was that more people took vitamin E. But not just any people. The new adopters were more educated, richer, more likely to exercise, less likely to smoke, more likely to eat vegetables. In turn, over time, as these people started taking the vitamin, vitamin E started to look even better for health.

Over a period of about a decade, vitamin E went from being only mildly associated with lower mortality to being strongly associated with lower mortality. This is not because the impacts of the vitamin changed! It was because the people who took the vitamin changed. And, importantly, these patterns persisted even when I put in controls.

What this says to me is that these biases in our conclusions — and I saw this in vitamins, but also in sugar and fat — are malleable based on the information out there in the world. Once you acknowledge that what is going on here is people are reading news and reacting to it in different ways, it is hard to believe that the limited observable characteristics we can control for are enough.

Evolving coefficients: Breastfeeding

The second important data point for me is looking carefully at what happens in many of these situations when we introduce more and better controls.

The link between breastfeeding and IQ is a good example. This is a research space where you can find many, many papers showing a positive correlation. The concern, of course, is that moms who breastfeed tend to be more educated, have higher income, and have access to more resources. These variables are also known to be linked to IQ, so it’s difficult to isolate the impacts of breastfeeding.

What these papers typically do is control for some observable differences. And, like the discussion above, we might think, “Well, isn’t that enough? If we can see these detailed demographics, isn’t that going to address the problem?”

The paper I like the best to illustrate the fact that, no, that doesn’t address the problem is one that used data that — among other things — included sibling pairs. The authors of this paper do four analyses of the relationship between breastfeeding and IQ:

  1. Raw correlation — no adjustment for anything
  2. Regression adjusting for standard demographics (parental education, etc.)
  3. Regression adjusting for standard demographics plus adjusting for mom IQ score
  4. Within-sibling analysis: compare two siblings, one of whom was breastfed and one of whom was not

The graph below shows their results. When they just compare groups — without adjusting for any other differences — there is a large difference in IQ between breastfed and non-breastfed children. When they add in some demographic adjustments, this difference falls but is still statistically significant. This is where most papers stop. But as these authors add their additional controls, eventually they get to an effect of zero. Comparing across siblings, there is no difference at all.

The point of this discussion is not to get in the weeds on breastfeeding (you can read my whole chapter from Cribsheet about it). This is an illustrative example of a general issue: the control sets we typically consider are incomplete. There are a lot of papers that report effectively only the first two bars in the graph above. But those simple observable controls are just not sufficient. The residual confounding is real and it is significant.

(If you want another example, you can look back to the very similar kind of graph in Panic Headlines from last week. This problem is everywhere.)

Conclusions

The question of whether a controlled effect in observational data is “causal” is inherently unanswerable. We are worried about differences between people that we cannot observe in the data. We can’t see them, so we must speculate about whether they are there. Based on a couple of decades of working intensely on these questions in both my research and my popular writing, I think they are almost always there. I think they are almost always important, and that a huge share of the correlations we see in observational data are not close to causal.

There are two final notes on this.

First: A common approach in these papers is to hedge in the conclusion by saying, “Well, it might not be causal.” I find this hedge problematic. If the relationship between processed food and cancer isn’t causal, why do we care about it? The obvious interpretation of this result is that you should stop eating processed foods. But if the result isn’t causal, that interpretation is wrong. This hedge is a cop-out. And this approach — to bury the hedge in the conclusion — encourages the poorly informed and inflammatory media coverage that often follows.

Second: I recognize that other people may disagree and find these relationships more compelling. I believe we can have productive conversations about that. To my mind, though, these conversations need to be grounded in the theory I started with. That is, if you want to argue that there is a causal relationship between processed food and cancer, you need to be willing to make a case that you’re approximating a randomized trial with your analysis. If we focus our discussion on that claim, it will discipline our disagreements.

And last: Thank you for indulging my love of econometrics today. My dad may be the only person who gets this far in the newsletter, but even so, it was worth it. Back with more parenting content on Thursday.

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We are better writers than influencers, I promise. Thanks to our kids for filming our unboxing videos. People make this look way too easy. 

Only two weeks until our book “The Unexpected” is here! Preorder at the link in my bio. 💙

We are better writers than influencers, I promise. Thanks to our kids for filming our unboxing videos. People make this look way too easy.

Only two weeks until our book “The Unexpected” is here! Preorder at the link in my bio. 💙
...

Exciting news! We have new, high-quality data that says it’s safe to take Tylenol during pregnancy and there is no link between Tylenol exposure and neurodevelopmental issues in kids. Comment “Link” for a DM to an article exploring this groundbreaking study.

While doctors have long said Tylenol was safe, confusing studies, panic headlines, and even a lawsuit have continually stoked fears in parents. As a result, many pregnant women have chosen not to take it, even if it would help them.

This is why good data is so important! When we can trust the data, we can trust our choices. And this study shows there is no blame to be placed on pregnant women here. So if you have a migraine or fever, please take your Tylenol.

#tylenol #pregnancy #pregnancyhealth #pregnancytips #parentdata #emilyoster

Exciting news! We have new, high-quality data that says it’s safe to take Tylenol during pregnancy and there is no link between Tylenol exposure and neurodevelopmental issues in kids. Comment “Link” for a DM to an article exploring this groundbreaking study.

While doctors have long said Tylenol was safe, confusing studies, panic headlines, and even a lawsuit have continually stoked fears in parents. As a result, many pregnant women have chosen not to take it, even if it would help them.

This is why good data is so important! When we can trust the data, we can trust our choices. And this study shows there is no blame to be placed on pregnant women here. So if you have a migraine or fever, please take your Tylenol.

#tylenol #pregnancy #pregnancyhealth #pregnancytips #parentdata #emilyoster
...

How many words should kids say — and when? Comment “Link” for a DM to an article about language development!

For this graph, researchers used a standardized measure of vocabulary size. Parents were given a survey and checked off all the words and sentences they have heard their child say.

They found that the average child—the 50th percentile line—at 24 months has about 300 words. A child at the 10th percentile—near the bottom of the distribution—has only about 50 words. On the other end, a child at the 90th percentile has close to 600 words. One main takeaway from these graphs is the explosion of language after fourteen or sixteen months. 

What’s valuable about this data is it can give us something beyond a general guideline about when to consider early intervention, and also provide reassurance that there is a significant range in this distribution at all young ages. 

#cribsheet #emilyoster #parentdata #languagedevelopment #firstwords

How many words should kids say — and when? Comment “Link” for a DM to an article about language development!

For this graph, researchers used a standardized measure of vocabulary size. Parents were given a survey and checked off all the words and sentences they have heard their child say.

They found that the average child—the 50th percentile line—at 24 months has about 300 words. A child at the 10th percentile—near the bottom of the distribution—has only about 50 words. On the other end, a child at the 90th percentile has close to 600 words. One main takeaway from these graphs is the explosion of language after fourteen or sixteen months.

What’s valuable about this data is it can give us something beyond a general guideline about when to consider early intervention, and also provide reassurance that there is a significant range in this distribution at all young ages.

#cribsheet #emilyoster #parentdata #languagedevelopment #firstwords
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I saw this and literally laughed out loud 😂 Thank you @adamgrant for sharing this gem! Someone let me know who originally created this masterpiece so I can give them the proper credit.

I saw this and literally laughed out loud 😂 Thank you @adamgrant for sharing this gem! Someone let me know who originally created this masterpiece so I can give them the proper credit. ...

Perimenopause comes with a whole host of symptoms, like brain fog, low sex drive, poor energy, and loss of muscle mass. These symptoms can be extremely bothersome and hard to treat. Could testosterone help? Comment “Link” for a DM to an article about the data on testosterone treatment for women in perimenopause.

#perimenopause #perimenopausehealth #womenshealth #hormoneimbalance #emilyoster #parentdata

Perimenopause comes with a whole host of symptoms, like brain fog, low sex drive, poor energy, and loss of muscle mass. These symptoms can be extremely bothersome and hard to treat. Could testosterone help? Comment “Link” for a DM to an article about the data on testosterone treatment for women in perimenopause.

#perimenopause #perimenopausehealth #womenshealth #hormoneimbalance #emilyoster #parentdata
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What age is best to start swim lessons? Comment “Link” for a DM to an article about water safety for children 💦

Summer is quickly approaching! You might be wondering if it’s the right time to have your kid start swim lessons. The AAP recommends starting between 1 and 4 years old. This is largely based on a randomized trial where young children were put into 8 or 12 weeks of swim lessons. They found that swimming ability and water safety reactions improve in both groups, and more so in the 12 weeks group.

Below this age range though, they are too young to actually learn how to swim. It’s fine to bring your baby into the pool (if you’re holding them) and they might like the water. But starting formal safety-oriented swim lessons before this age isn’t likely to be very helpful.

Most importantly, no matter how old your kid is or how good of a swimmer they are, adult supervision is always necessary!

#swimlessons #watersafety #kidsswimminglessons #poolsafety #emilyoster #parentdata

What age is best to start swim lessons? Comment “Link” for a DM to an article about water safety for children 💦

Summer is quickly approaching! You might be wondering if it’s the right time to have your kid start swim lessons. The AAP recommends starting between 1 and 4 years old. This is largely based on a randomized trial where young children were put into 8 or 12 weeks of swim lessons. They found that swimming ability and water safety reactions improve in both groups, and more so in the 12 weeks group.

Below this age range though, they are too young to actually learn how to swim. It’s fine to bring your baby into the pool (if you’re holding them) and they might like the water. But starting formal safety-oriented swim lessons before this age isn’t likely to be very helpful.

Most importantly, no matter how old your kid is or how good of a swimmer they are, adult supervision is always necessary!

#swimlessons #watersafety #kidsswimminglessons #poolsafety #emilyoster #parentdata
...

Can babies have salt? 🧂 While babies don’t need extra salt beyond what’s in breast milk or formula, the risks of salt toxicity from normal foods are minimal. There are concerns about higher blood pressure in the long term due to a higher salt diet in the first year, but the data on these is not super compelling and the differences are small.

Like with most things, moderation is key! Avoid very salty chips or olives or saltines with your infant. But if you’re doing baby-led weaning, it’s okay for them to share your lightly salted meals. Your baby does not need their own, unsalted, chicken if you’re making yourself a roast. Just skip the super salty stuff.

 #emilyoster #parentdata #childnutrition #babynutrition #foodforkids

Can babies have salt? 🧂 While babies don’t need extra salt beyond what’s in breast milk or formula, the risks of salt toxicity from normal foods are minimal. There are concerns about higher blood pressure in the long term due to a higher salt diet in the first year, but the data on these is not super compelling and the differences are small.

Like with most things, moderation is key! Avoid very salty chips or olives or saltines with your infant. But if you’re doing baby-led weaning, it’s okay for them to share your lightly salted meals. Your baby does not need their own, unsalted, chicken if you’re making yourself a roast. Just skip the super salty stuff.

#emilyoster #parentdata #childnutrition #babynutrition #foodforkids
...

Is sleep training bad? Comment “Link” for a DM to an article breaking down the data on sleep training 😴

Among parenting topics, sleep training is one of the most divisive. Ultimately, it’s important to know that studies looking at the short- and long-term effects of sleep training show no evidence of harm. The data actually shows it can improve infant sleep and lower parental depression.

Even so, while sleep training can be a great option, it will not be for everyone. Just as people can feel judged for sleep training, they can feel judged for not doing it. Engaging in any parenting behavior because it’s what’s expected of you is not a good idea. You have to do what works best for your family! If that’s sleep training, make a plan and implement it. If not, that’s okay too.

What’s your experience with sleep training? Did you feel judged for your decision to do (or not do) it?

#sleeptraining #newparents #babysleep #emilyoster #parentdata

Is sleep training bad? Comment “Link” for a DM to an article breaking down the data on sleep training 😴

Among parenting topics, sleep training is one of the most divisive. Ultimately, it’s important to know that studies looking at the short- and long-term effects of sleep training show no evidence of harm. The data actually shows it can improve infant sleep and lower parental depression.

Even so, while sleep training can be a great option, it will not be for everyone. Just as people can feel judged for sleep training, they can feel judged for not doing it. Engaging in any parenting behavior because it’s what’s expected of you is not a good idea. You have to do what works best for your family! If that’s sleep training, make a plan and implement it. If not, that’s okay too.

What’s your experience with sleep training? Did you feel judged for your decision to do (or not do) it?

#sleeptraining #newparents #babysleep #emilyoster #parentdata
...

Does your kid love to stall right before bedtime? 💤 Tell me more about their tactics in the comments below!

#funnytweets #bedtime #nightimeroutine #parentinghumor #parentingmemes

Does your kid love to stall right before bedtime? 💤 Tell me more about their tactics in the comments below!

#funnytweets #bedtime #nightimeroutine #parentinghumor #parentingmemes
...

Got a big decision to make? 🤔 Comment “Link” for a DM to read about my easy mantra for making hard choices. 

When we face a complicated problem in pregnancy or parenting, and don’t like either option A or B, we often wait around for a secret third option to reveal itself. This magical thinking, as appealing as it is, gets in the way. We need a way to remind ourselves that we need to make an active choice, even if it is hard. The mantra I use for this: “There is no secret option C.”

Having this realization, accepting it, reminding ourselves of it, can help us make the hard decisions and accurately weigh the risks and benefits of our choices.

#parentingquotes #decisionmaking #nosecretoptionc #parentingadvice #emilyoster #parentdata

Got a big decision to make? 🤔 Comment “Link” for a DM to read about my easy mantra for making hard choices.

When we face a complicated problem in pregnancy or parenting, and don’t like either option A or B, we often wait around for a secret third option to reveal itself. This magical thinking, as appealing as it is, gets in the way. We need a way to remind ourselves that we need to make an active choice, even if it is hard. The mantra I use for this: “There is no secret option C.”

Having this realization, accepting it, reminding ourselves of it, can help us make the hard decisions and accurately weigh the risks and benefits of our choices.

#parentingquotes #decisionmaking #nosecretoptionc #parentingadvice #emilyoster #parentdata
...

Excuse the language, but I have such strong feelings about this subject! Sometimes, it feels like there’s no winning as a mother. People pressure you to breastfeed and, in the same breath, shame you for doing it in public. Which is it?!

So yes, they’re being completely unreasonable. You should be able to feed your baby in peace. What are some responses you can give to someone who tells you to cover up? Share in the comments below ⬇️

#breastfeeding #breastfeedinginpublic #breastfeedingmom #motherhood #emilyoster

Excuse the language, but I have such strong feelings about this subject! Sometimes, it feels like there’s no winning as a mother. People pressure you to breastfeed and, in the same breath, shame you for doing it in public. Which is it?!

So yes, they’re being completely unreasonable. You should be able to feed your baby in peace. What are some responses you can give to someone who tells you to cover up? Share in the comments below ⬇️

#breastfeeding #breastfeedinginpublic #breastfeedingmom #motherhood #emilyoster
...

Potty training can feel like a Mount Everest-size challenge, and sadly, our evidence-based guidance is poor. So, I created a survey to collate advice and feedback on success from about 6,000 participants.

How long does potty training take? We found that there is a strong basic pattern here: the later you wait to start, the shorter time it takes to potty train. On average, people who start at under 18 months report it takes them about 12 weeks for their child to be fully trained (using the toilet consistently for both peeing and pooping). For those who start between 3 and 3.5, it’s more like nine days. Keep in mind that for all of these age groups, there is a range of length of time from a few days to over a year. Sometimes parents are told that if you do it right, it only takes a few days. While that is true for some people, it is definitely not the norm.

If you’re in the throes of potty training, hang in there! 

#emilyoster #parentdata #pottytraining #pottytrainingtips #toddlerlife

Potty training can feel like a Mount Everest-size challenge, and sadly, our evidence-based guidance is poor. So, I created a survey to collate advice and feedback on success from about 6,000 participants.

How long does potty training take? We found that there is a strong basic pattern here: the later you wait to start, the shorter time it takes to potty train. On average, people who start at under 18 months report it takes them about 12 weeks for their child to be fully trained (using the toilet consistently for both peeing and pooping). For those who start between 3 and 3.5, it’s more like nine days. Keep in mind that for all of these age groups, there is a range of length of time from a few days to over a year. Sometimes parents are told that if you do it right, it only takes a few days. While that is true for some people, it is definitely not the norm.

If you’re in the throes of potty training, hang in there!

#emilyoster #parentdata #pottytraining #pottytrainingtips #toddlerlife
...

For children or adults with severe food allergies, they can be incredibly scary and restrictive. We may imagine that it’s easy to deal with a peanut allergy by, say, not eating peanut butter sandwiches. But for someone with a severe version of this allergy, they may never be able to go to a restaurant, for fear of a severe reaction to something in the air. Right now, there’s only one approved treatment for severe allergies like this and it’s limited to peanuts.

This is why the new medication Xolair is very exciting. It promises a second possible treatment avenue and one that works for other allergens. A new trail analyzed data from 177 children with severe food allergies. Two-thirds of the treatment group were able to tolerate the specified endpoint, versus just 7% of the placebo group. This is a very large treatment effect, and the authors found similarly large impacts on other allergens. 

There are some caveats: This treatment won’t work for everyone. (One-third of participants did not respond to it.) Additionally, this treatment is an injection given every two to four weeks, indefinitely. This may make it less palatable to children. 

Overall, even with caveats, this is life-changing news for many families!

#xolair #foodallergies #allergies #peanutallergy #emilyoster #parentdata

For children or adults with severe food allergies, they can be incredibly scary and restrictive. We may imagine that it’s easy to deal with a peanut allergy by, say, not eating peanut butter sandwiches. But for someone with a severe version of this allergy, they may never be able to go to a restaurant, for fear of a severe reaction to something in the air. Right now, there’s only one approved treatment for severe allergies like this and it’s limited to peanuts.

This is why the new medication Xolair is very exciting. It promises a second possible treatment avenue and one that works for other allergens. A new trail analyzed data from 177 children with severe food allergies. Two-thirds of the treatment group were able to tolerate the specified endpoint, versus just 7% of the placebo group. This is a very large treatment effect, and the authors found similarly large impacts on other allergens.

There are some caveats: This treatment won’t work for everyone. (One-third of participants did not respond to it.) Additionally, this treatment is an injection given every two to four weeks, indefinitely. This may make it less palatable to children.

Overall, even with caveats, this is life-changing news for many families!

#xolair #foodallergies #allergies #peanutallergy #emilyoster #parentdata
...

If you have a fever during pregnancy, you should take Tylenol, both because it will make you feel better and because of concerns about fever in pregnancy (although these are also overstated).

The evidence that suggests risks to Tylenol focuses largely on more extensive exposure — say, taking it for more than 28 days during pregnancy. There is no credible evidence, even correlational, to suggest that taking it occasionally for a fever or headache would be an issue.

People take Tylenol for a reason. For many people, the choice may be between debilitating weekly migraines and regular Tylenol usage. The impacts studies suggest are very small. In making this decision, we should weigh the real, known benefit against the suggestion of this possible risk. Perhaps not everyone will come out at the same place on this, but it is crucial we give people the tools to make the choice for themselves.

#emilyoster #parentdata #tylenol #pregnancy #pregnancytips

If you have a fever during pregnancy, you should take Tylenol, both because it will make you feel better and because of concerns about fever in pregnancy (although these are also overstated).

The evidence that suggests risks to Tylenol focuses largely on more extensive exposure — say, taking it for more than 28 days during pregnancy. There is no credible evidence, even correlational, to suggest that taking it occasionally for a fever or headache would be an issue.

People take Tylenol for a reason. For many people, the choice may be between debilitating weekly migraines and regular Tylenol usage. The impacts studies suggest are very small. In making this decision, we should weigh the real, known benefit against the suggestion of this possible risk. Perhaps not everyone will come out at the same place on this, but it is crucial we give people the tools to make the choice for themselves.

#emilyoster #parentdata #tylenol #pregnancy #pregnancytips
...

Parenting trends are like Cabbage Patch Kids: they’re usually only popular because a bunch of people are using them! Most of the time, these trends are not based on new scientific research, and even if they are, that new research doesn’t reflect all of what we’ve studied before.

In the future, before hopping onto the latest trend, check the data first. Unlike Cabbage Patch Kids, parenting trends can add a lot of unnecessary stress and challenges to your plate. What’s a recent trend that you’ve been wondering about?

#parentdata #emilyoster #parentingtips #parentingadvice #parentinghacks

Parenting trends are like Cabbage Patch Kids: they’re usually only popular because a bunch of people are using them! Most of the time, these trends are not based on new scientific research, and even if they are, that new research doesn’t reflect all of what we’ve studied before.

In the future, before hopping onto the latest trend, check the data first. Unlike Cabbage Patch Kids, parenting trends can add a lot of unnecessary stress and challenges to your plate. What’s a recent trend that you’ve been wondering about?

#parentdata #emilyoster #parentingtips #parentingadvice #parentinghacks
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As of this week, 1 million copies of my books have been sold. This feels humbling and, frankly, unbelievable. I’m so thankful to those of you who’ve read and passed along your recommendations of the books.

When I wrote Expecting Better, I had no plan for all of this — I wrote that book because I felt compelled to write it, because it was the book I wanted to read. As I’ve come out with more books, and now ParentData, I am closer to seeing what I hope we can all create. That is: a world where everyone has access to reliable data, based on causal evidence, to make informed, confident decisions that work for their families.

I’m so grateful you’re all here as a part of this, and I want to thank you! If you’ve been waiting for the right moment to sign up for full access to ParentData, this is it. ⭐️ Comment “Link” for a DM with a discount code for 20% off of a new monthly or annual subscription to ParentData! 

Thank you again for being the best community of readers and internet-friends on the planet. I am so lucky to have you all here.

#parentdata #emilyoster #expectingbetter #cribsheet #familyfirm #parentingcommunity

As of this week, 1 million copies of my books have been sold. This feels humbling and, frankly, unbelievable. I’m so thankful to those of you who’ve read and passed along your recommendations of the books.

When I wrote Expecting Better, I had no plan for all of this — I wrote that book because I felt compelled to write it, because it was the book I wanted to read. As I’ve come out with more books, and now ParentData, I am closer to seeing what I hope we can all create. That is: a world where everyone has access to reliable data, based on causal evidence, to make informed, confident decisions that work for their families.

I’m so grateful you’re all here as a part of this, and I want to thank you! If you’ve been waiting for the right moment to sign up for full access to ParentData, this is it. ⭐️ Comment “Link” for a DM with a discount code for 20% off of a new monthly or annual subscription to ParentData!

Thank you again for being the best community of readers and internet-friends on the planet. I am so lucky to have you all here.

#parentdata #emilyoster #expectingbetter #cribsheet #familyfirm #parentingcommunity
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Just eat your Cheerios and move on.

Just eat your Cheerios and move on. ...

The AAP’s guidelines recommend sleeping in the same room as your baby “ideally for the first six months.” However, the risk of SIDS is dramatically lower after four months, and the evidence in favor of the protective effect of room sharing is quite weak (both overall and even more so after four months). There is also growing evidence that infants who sleep in their own room by four months sleep better at four months, better at nine months, and even better at 30 months.

With this in mind, it’s worth asking why this recommendation continues at all — or at least why the AAP doesn’t push it back to four months. They say decreased arousals from sleep are linked to SIDS, which could mean that babies sleeping in their own room is risky. But this link is extremely indirect, and they do not show direct evidence to support it.

According to the data we have, parents should sleep in the same room as a baby for as long as it works for them! Sharing a room with a child may have negative impacts on both child and adult sleep. We should give families more help in navigating these trade-offs and making the decisions that work best for them.

#emilyoster #parentdata #roomsharing #sids #parentingguide

The AAP’s guidelines recommend sleeping in the same room as your baby “ideally for the first six months.” However, the risk of SIDS is dramatically lower after four months, and the evidence in favor of the protective effect of room sharing is quite weak (both overall and even more so after four months). There is also growing evidence that infants who sleep in their own room by four months sleep better at four months, better at nine months, and even better at 30 months.

With this in mind, it’s worth asking why this recommendation continues at all — or at least why the AAP doesn’t push it back to four months. They say decreased arousals from sleep are linked to SIDS, which could mean that babies sleeping in their own room is risky. But this link is extremely indirect, and they do not show direct evidence to support it.

According to the data we have, parents should sleep in the same room as a baby for as long as it works for them! Sharing a room with a child may have negative impacts on both child and adult sleep. We should give families more help in navigating these trade-offs and making the decisions that work best for them.

#emilyoster #parentdata #roomsharing #sids #parentingguide
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