{"id":1794,"date":"2026-05-01T19:34:53","date_gmt":"2026-05-01T19:34:53","guid":{"rendered":"https:\/\/www.allendowney.com\/blog\/?p=1794"},"modified":"2026-05-01T19:34:55","modified_gmt":"2026-05-01T19:34:55","slug":"planning-for-your-midlife-crisis","status":"publish","type":"post","link":"https:\/\/www.allendowney.com\/blog\/2026\/05\/01\/planning-for-your-midlife-crisis\/","title":{"rendered":"Planning for your midlife crisis"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">Yesterday I presented a talk at ODSC East 2026, called &#8220;Counterfactual Analysis with Bayesian Models: What Drives the Life Expectancy Gap?&#8221; Here&#8217;s the abstract<\/p>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p class=\"wp-block-paragraph\">Across nearly every country in the world, women live longer than men\u2014but the size of this gap varies from about two years in some countries to more than twelve in others. What explains these differences, and how much of the gap can be closed?<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">In this talk, I present a practical approach to counterfactual analysis using Bayesian regression models. Using publicly available mortality data, we build a model that relates the life expectancy gap between men and women to differences in cause-specific death rates, including homicide, drug overdoses, traffic fatalities, smoking-related disease, and chronic illness.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The model generates posterior simulations that answer \u201cwhat-if\u201d questions. For example: How much smaller would the U.S. life expectancy gap be if homicide rates matched those in Western Europe?<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The talk presents the workflow\u2014from assembling global datasets to fitting interpretable Bayesian models with PyMC and generating counterfactual simulations. Attendees will learn how Bayesian models can support explainable modeling and analysis under uncertainty.<\/p>\n<\/blockquote>\n\n\n\n<p class=\"wp-block-paragraph\">I think the talk went well, and we got some good questions at the end. There&#8217;s no recording, unfortunately, <a href=\"https:\/\/docs.google.com\/presentation\/d\/e\/2PACX-1vSiwjA6Mi95poqGQJiXw7Mdy0BgPa-aEmVhJHXwP35QRjt2dduEEN53yZi4fnpAReVaS-EDWS3CwMt1\/pub?start=false&amp;loop=false&amp;delayms=3000\">but my slides are here<\/a>. And if you want to know more, I have a series of blog posts on Substack<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a href=\"https:\/\/allendowney.substack.com\/p\/the-gender-gap-in-life-expectancy\">The Gender Gap in Life Expectancy<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/allendowney.substack.com\/p\/what-drives-the-life-expectancy-gender\">What Drives the Life Expectancy Gender Gap?<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/allendowney.substack.com\/p\/how-much-of-the-life-expectancy-gap\">How Much of the Life Expectancy Gap Could We Close?<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/allendowney.substack.com\/p\/international-patterns-in-the-life\">International Patterns in the Life Expectancy Gap<\/a><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">The fifth and final post is on the way. In the meantime, here&#8217;s a quick post on a related topic.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Are you middle-aged?<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Here\u2019s a question from Reddit\u2019s <a href=\"https:\/\/www.reddit.com\/r\/stupidquestions\/comments\/1sxesux\/what_age_is_actually_considered_middle_age\/\">Stupid Questions forum<\/a>:<\/p>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p class=\"wp-block-paragraph\">I always thought middle age was in your 40s but since life expectancy is around 75 or so, wouldn\u2019t it be about 35?<\/p>\n<\/blockquote>\n\n\n\n<p class=\"wp-block-paragraph\">If life expectancy is 75, you might think the midpoint is half that, which is 37.5. But if 75 is life expectancy <em>at birth<\/em> and you survive to age 37.5, your life expectancy <em>at that age<\/em> is higher than 75. So 37.5 is not halfway!<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">If we really want to find the midpoint \u2013 and it wouldn\u2019t be <em>Probably Overthinking It<\/em> if we didn\u2019t \u2013 we have to find the age where your expected remaining lifetime equals your current age.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Let\u2019s do it.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Data<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">From the <a href=\"https:\/\/www.mortality.org\/Country\/Country?cntr=USA\">Human Mortality Database<\/a> I downloaded life tables for the United States, combined and broken down for men and women. The following function reads and cleans a table.<\/p>\n\n\n\n<pre id=\"codecell1\" class=\"wp-block-preformatted\">def read_life_table(filename):\n    lt = pd.read_fwf(filename, skiprows=2, infer_nrows=200)\n    lt['Age'] = lt['Age'].str.replace('+', '', regex=False).astype(int)\n    return lt\n<\/pre>\n\n\n\n<p class=\"wp-block-paragraph\">Here are the first few rows of the combined table (see notes below for details).<\/p>\n\n\n\n<pre id=\"codecell2\" class=\"wp-block-preformatted\">blt = read_life_table('..\/data\/bltper_1x1.txt')\nblt.head()\n<\/pre>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><thead><tr><th><\/th><th>Year<\/th><th>Age<\/th><th>mx<\/th><th>qx<\/th><th>ax<\/th><th>lx<\/th><th>dx<\/th><th>Lx<\/th><th>Tx<\/th><th>ex<\/th><\/tr><\/thead><tbody><tr><th>0<\/th><td>1933<\/td><td>0<\/td><td>0.06129<\/td><td>0.05861<\/td><td>0.25<\/td><td>100000<\/td><td>5861<\/td><td>95624<\/td><td>6089609<\/td><td>60.90<\/td><\/tr><tr><th>1<\/th><td>1933<\/td><td>1<\/td><td>0.00946<\/td><td>0.00941<\/td><td>0.50<\/td><td>94139<\/td><td>886<\/td><td>93696<\/td><td>5993985<\/td><td>63.67<\/td><\/tr><tr><th>2<\/th><td>1933<\/td><td>2<\/td><td>0.00435<\/td><td>0.00434<\/td><td>0.50<\/td><td>93253<\/td><td>405<\/td><td>93050<\/td><td>5900289<\/td><td>63.27<\/td><\/tr><tr><th>3<\/th><td>1933<\/td><td>3<\/td><td>0.00310<\/td><td>0.00310<\/td><td>0.50<\/td><td>92848<\/td><td>288<\/td><td>92704<\/td><td>5807239<\/td><td>62.55<\/td><\/tr><tr><th>4<\/th><td>1933<\/td><td>4<\/td><td>0.00239<\/td><td>0.00238<\/td><td>0.50<\/td><td>92560<\/td><td>221<\/td><td>92450<\/td><td>5714535<\/td><td>61.74<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">We\u2019ll also read the female and male tables.<\/p>\n\n\n\n<pre id=\"codecell3\" class=\"wp-block-preformatted\">flt = read_life_table('..\/data\/fltper_1x1.txt')\n<\/pre>\n\n\n\n<pre id=\"codecell4\" class=\"wp-block-preformatted\">mlt = read_life_table('..\/data\/mltper_1x1.txt')\n<\/pre>\n\n\n\n<p class=\"wp-block-paragraph\">The tables include data from 1933 to 2024, so we\u2019ll select the most recent data.<\/p>\n\n\n\n<pre id=\"codecell5\" class=\"wp-block-preformatted\">year = blt['Year'].unique()[-1]\ntable = blt.query('Year == @year').set_index('Age')\n<\/pre>\n\n\n\n<p class=\"wp-block-paragraph\">The column we\u2019ll use is <code>ex<\/code>, which is life expectancy as a function of age.<\/p>\n\n\n\n<pre id=\"codecell6\" class=\"wp-block-preformatted\">age = table.index.to_series()\nex = table['ex']\n<\/pre>\n\n\n\n<p class=\"wp-block-paragraph\">Life expectancy at birth is 79 years, so the naive midpoint is 39.5.<\/p>\n\n\n\n<pre id=\"codecell7\" class=\"wp-block-preformatted\">ex[0], ex[0] \/ 2\n<\/pre>\n\n\n\n<pre id=\"codecell8\" class=\"wp-block-preformatted\">(79.08, 39.54)\n<\/pre>\n\n\n\n<p class=\"wp-block-paragraph\">But at age 40, expected remaining lifetime is 41.1, so 39.5 is not the midpoint.<\/p>\n\n\n\n<pre id=\"codecell9\" class=\"wp-block-preformatted\">ex[39], ex[40]\n<\/pre>\n\n\n\n<pre id=\"codecell10\" class=\"wp-block-preformatted\">(42.04, 41.12)\n<\/pre>\n\n\n\n<p class=\"wp-block-paragraph\">This plot shows life expectancy at each age, compared to age.<\/p>\n\n\n\n<pre id=\"codecell11\" class=\"wp-block-preformatted\">ex.plot(label='Remaining life expectancy')\nage.plot(label='Age')\ndecorate(ylabel='Years',\n        title='Remaining life expectancy vs age, United States 2024')\n<\/pre>\n\n\n\n<figure class=\"wp-block-image\"><img loading=\"lazy\" decoding=\"async\" width=\"438\" height=\"250\" src=\"https:\/\/www.allendowney.com\/blog\/wp-content\/uploads\/2026\/05\/a55e9cbde25b36d9c42e2ede2a78a827460d537f230b5cc4e7dfcb519a44bbc6.png\" alt=\"_images\/a55e9cbde25b36d9c42e2ede2a78a827460d537f230b5cc4e7dfcb519a44bbc6.png\" class=\"wp-image-1797\" srcset=\"https:\/\/www.allendowney.com\/blog\/wp-content\/uploads\/2026\/05\/a55e9cbde25b36d9c42e2ede2a78a827460d537f230b5cc4e7dfcb519a44bbc6.png 438w, https:\/\/www.allendowney.com\/blog\/wp-content\/uploads\/2026\/05\/a55e9cbde25b36d9c42e2ede2a78a827460d537f230b5cc4e7dfcb519a44bbc6-300x171.png 300w\" sizes=\"auto, (max-width: 438px) 100vw, 438px\" \/><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">\u201cMiddle age\u201d is where the lines cross, which we can compute by linear interpolation.<\/p>\n\n\n\n<pre id=\"codecell12\" class=\"wp-block-preformatted\">from scipy.interpolate import interp1d\n\ninverse = interp1d(ex - age, age)\ninverse(0)\n<\/pre>\n\n\n\n<pre id=\"codecell13\" class=\"wp-block-preformatted\">array(40.58638743)\n<\/pre>\n\n\n\n<p class=\"wp-block-paragraph\">So the overall midpoint is 40.6 years. But as you might expect, it\u2019s different for men and women. Let\u2019s put the analysis we did in a function.<\/p>\n\n\n\n<pre id=\"codecell14\" class=\"wp-block-preformatted\">def get_midpoint(filename):\n    lt = read_life_table(filename)\n    year = lt['Year'].unique()[-1]\n    table = lt.query('Year == @year').set_index('Age')\n\n    age = pd.Series(table.index)\n    ex = table['ex']\n\n    inverse = interp1d(ex - age, age)\n    return inverse(0)\n<\/pre>\n\n\n\n<p class=\"wp-block-paragraph\">And run it for men.<\/p>\n\n\n\n<pre id=\"codecell15\" class=\"wp-block-preformatted\">get_midpoint('..\/data\/mltper_1x1.txt')\n<\/pre>\n\n\n\n<pre id=\"codecell16\" class=\"wp-block-preformatted\">array(39.57142857)\n<\/pre>\n\n\n\n<p class=\"wp-block-paragraph\">And women.<\/p>\n\n\n\n<pre id=\"codecell17\" class=\"wp-block-preformatted\">get_midpoint('..\/data\/fltper_1x1.txt')\n<\/pre>\n\n\n\n<pre id=\"codecell18\" class=\"wp-block-preformatted\">array(41.56185567)\n<\/pre>\n\n\n\n<p class=\"wp-block-paragraph\">Men hit middle age at 39.6, women at 41.6.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">The Gender Gap and Age<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Finally, let\u2019s see how the gender gap in life expectancy changes as a function of age.<\/p>\n\n\n\n<pre id=\"codecell19\" class=\"wp-block-preformatted\">ex_male = mlt.query('Year == @year').set_index('Age')['ex']\nex_female = flt.query('Year == @year').set_index('Age')['ex']\ngap = ex_female - ex_male\n<\/pre>\n\n\n\n<pre id=\"codecell20\" class=\"wp-block-preformatted\">gap.plot(label='')\ndecorate(ylabel='Years',\n         title='Life expectancy gender gap vs age')\n<\/pre>\n\n\n\n<figure class=\"wp-block-image\"><img loading=\"lazy\" decoding=\"async\" width=\"438\" height=\"250\" src=\"https:\/\/www.allendowney.com\/blog\/wp-content\/uploads\/2026\/05\/2cf48be5acc971ad8f6973506c224767a25ef2e38bcf53c5b12b0f895c26ed11.png\" alt=\"_images\/2cf48be5acc971ad8f6973506c224767a25ef2e38bcf53c5b12b0f895c26ed11.png\" class=\"wp-image-1796\" srcset=\"https:\/\/www.allendowney.com\/blog\/wp-content\/uploads\/2026\/05\/2cf48be5acc971ad8f6973506c224767a25ef2e38bcf53c5b12b0f895c26ed11.png 438w, https:\/\/www.allendowney.com\/blog\/wp-content\/uploads\/2026\/05\/2cf48be5acc971ad8f6973506c224767a25ef2e38bcf53c5b12b0f895c26ed11-300x171.png 300w\" sizes=\"auto, (max-width: 438px) 100vw, 438px\" \/><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">At birth the life expectancy gap is close to five years. At age 100, it is close to zero.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">But just looking at the gap might be misleading. For a more complete picture let\u2019s also look at the ratio.<\/p>\n\n\n\n<pre id=\"codecell21\" class=\"wp-block-preformatted\">ratio = ex_female \/ ex_male\nratio.plot(label='')\n\ndecorate(ylabel='Ratio',\n         title='Life expectancy gender ratio (female \/ male)')\n<\/pre>\n\n\n\n<figure class=\"wp-block-image\"><img loading=\"lazy\" decoding=\"async\" width=\"446\" height=\"250\" src=\"https:\/\/www.allendowney.com\/blog\/wp-content\/uploads\/2026\/05\/1a514d22895c3e18a767184700d6edf467975abb4b302afe044fd42c002ae21c.png\" alt=\"_images\/1a514d22895c3e18a767184700d6edf467975abb4b302afe044fd42c002ae21c.png\" class=\"wp-image-1798\" srcset=\"https:\/\/www.allendowney.com\/blog\/wp-content\/uploads\/2026\/05\/1a514d22895c3e18a767184700d6edf467975abb4b302afe044fd42c002ae21c.png 446w, https:\/\/www.allendowney.com\/blog\/wp-content\/uploads\/2026\/05\/1a514d22895c3e18a767184700d6edf467975abb4b302afe044fd42c002ae21c-300x168.png 300w\" sizes=\"auto, (max-width: 446px) 100vw, 446px\" \/><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">The life expectancy ratio tells a more complicated story.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>At birth, the ratio is 1.06, which means female babies live 6% longer, on average.<\/li>\n\n\n\n<li>Around age 80, the ratio peaks at nearly 1.14 \u2013 so between female and male octogenarians, we expect the women to live 14% longer.<\/li>\n\n\n\n<li>At advanced ages, the ratio declines steeply and actually crosses over after age 100 \u2013 although the crossover is minimal and might not be statistically valid.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">To interpret these results, we can think about the causes of death that contribute to age-specific death rates at different stages of life.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>In young adulthood, the causes of death that contribute most to gender gaps include road traffic, homicide, accidental injury, drug use disorders.<\/li>\n\n\n\n<li>In advanced adulthood, they include cancer, cardiovascular disease, respiratory disease, liver disease, diabetes, and suicide.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">The causes that affect younger people have large gender gaps, but relatively low death rates. As people get older, these low-rate causes contribute less to age-specific death rates, and the higher-rate causes contribute more.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">I think that\u2019s a plausible explanation for the increasing ratio from age 0 to 80. For the decline that follows, I can only speculate that there is a selection effect: people who get to these advanced ages are likely to have better-than-average lifestyle histories (less smoking and drinking, better diet, more exercise) \u2013 and among people with better lifestyles, the gender gap is small.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Notes<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Data credit: HMD. Human Mortality Database. Max Planck Institute for Demographic Research (Germany), University of California, Berkeley (USA), and French Institute for Demographic Studies (France). Available at [<a href=\"http:\/\/www.mortality.org\">www.mortality.org<\/a>].<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Here are the columns of the 1\u00d71 Period Life Tables:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Year<\/strong>: Calendar year to which the period life table refers.<\/li>\n\n\n\n<li><strong>Age<\/strong>: Exact age (x), in years, at the beginning of the interval ([x, x+1)).<\/li>\n\n\n\n<li><strong>mx<\/strong>: Central death rate at age (x):<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>qx<\/strong>: Probability of dying between ages (x) and (x+1):<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>ax<\/strong>: Average fraction of the interval lived by those who die in ([x, x+1)). Typically around 0.5 for most ages, lower for infants (reflecting higher early mortality within the year).<\/li>\n\n\n\n<li><strong>lx<\/strong>: Number of survivors at exact age (x), out of a radix (usually 100,000 births).<\/li>\n\n\n\n<li><strong>dx<\/strong>: Number of deaths between ages (x) and (x+1):<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Lx<\/strong>: Person-years lived between ages (x) and (x+1), approximately<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Tx<\/strong>: Total person-years remaining above age (x):<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>ex<\/strong>: Life expectancy at age (x):<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">The details are in<a href=\"https:\/\/github.com\/AllenDowney\/ProbablyOverthinkingIt\/blob\/book\/examples\/midlife.ipynb\"> this Jupyter notebook<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Yesterday I presented a talk at ODSC East 2026, called &#8220;Counterfactual Analysis with Bayesian Models: What Drives the Life Expectancy Gap?&#8221; Here&#8217;s the abstract Across nearly every country in the world, women live longer than men\u2014but the size of this gap varies from about two years in some countries to more than twelve in others. What explains these differences, and how much of the gap can be closed? In this talk, I present a practical approach to counterfactual analysis using&#8230;<\/p>\n<p class=\"read-more\"><a class=\"btn btn-default\" href=\"https:\/\/www.allendowney.com\/blog\/2026\/05\/01\/planning-for-your-midlife-crisis\/\"> Read More<span class=\"screen-reader-text\">  Read More<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2},"jetpack_post_was_ever_published":false},"categories":[1],"tags":[128,129,130],"class_list":["post-1794","post","type-post","status-publish","format-standard","hentry","category-uncategorized","tag-life-expectancy","tag-mortality","tag-odsc"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.7 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Planning for your midlife crisis - Probably Overthinking It<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.allendowney.com\/blog\/2026\/05\/01\/planning-for-your-midlife-crisis\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Planning for your midlife crisis - Probably Overthinking It\" \/>\n<meta property=\"og:description\" content=\"Yesterday I presented a talk at ODSC East 2026, called &#8220;Counterfactual Analysis with Bayesian Models: What Drives the Life Expectancy Gap?&#8221; Here&#8217;s the abstract Across nearly every country in the world, women live longer than men\u2014but the size of this gap varies from about two years in some countries to more than twelve in others. What explains these differences, and how much of the gap can be closed? In this talk, I present a practical approach to counterfactual analysis using... 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The video is available now. This workshop is based on the first session of the Applied Bayesian Modeling Workshop I teach along with my colleagues at PyMC Labs. If you would like to learn more, it\u2026","rel":"","context":"In \"bayesian statistics\"","block_context":{"text":"bayesian statistics","link":"https:\/\/www.allendowney.com\/blog\/tag\/bayesian-statistics\/"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/img.youtube.com\/vi\/PLGVZCDnMOq0qmerwB1eITnr5AfYRGm0DF\/0.jpg?resize=350%2C200","width":350,"height":200},"classes":[]},{"id":1664,"url":"https:\/\/www.allendowney.com\/blog\/2026\/03\/29\/attention-chinese-readers\/","url_meta":{"origin":1794,"position":4},"title":"Attention, Chinese Readers","author":"AllenDowney","date":"March 29, 2026","format":false,"excerpt":"The Chinese edition of Probably Overthinking It is available now (also here)! If you have the Chinese edition, there are two sections you won't get to read -- so I am including them here. Here is an excerpt from Chapter 3, including the deleted paragraph: In the Present The women\u2026","rel":"","context":"In \"book\"","block_context":{"text":"book","link":"https:\/\/www.allendowney.com\/blog\/tag\/book\/"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/www.allendowney.com\/blog\/wp-content\/uploads\/2026\/03\/6a45a65e6a7b3201af0f74c7b7df4d57ce5c5976972ce0e69538a1914fa5cc5b.png?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]},{"id":1357,"url":"https:\/\/www.allendowney.com\/blog\/2024\/08\/23\/probably-the-book\/","url_meta":{"origin":1794,"position":5},"title":"Probably the Book","author":"AllenDowney","date":"August 23, 2024","format":false,"excerpt":"Last week I had the pleasure of presenting a keynote at posit::conf(2024). When the video is available, I will post it here [UPDATE here it is]. https:\/\/www.youtube.com\/watch?v=YKMZIzYBgTk In the meantime, you can read the slides, if you don't mind spoilers. For people at the conference who don't know me, this\u2026","rel":"","context":"Similar post","block_context":{"text":"Similar post","link":""},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/www.allendowney.com\/blog\/wp-content\/uploads\/2024\/08\/are_you_normal_windshield_wiper.gif?resize=350%2C200&ssl=1","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/www.allendowney.com\/blog\/wp-content\/uploads\/2024\/08\/are_you_normal_windshield_wiper.gif?resize=350%2C200&ssl=1 1x, https:\/\/i0.wp.com\/www.allendowney.com\/blog\/wp-content\/uploads\/2024\/08\/are_you_normal_windshield_wiper.gif?resize=525%2C300&ssl=1 1.5x, https:\/\/i0.wp.com\/www.allendowney.com\/blog\/wp-content\/uploads\/2024\/08\/are_you_normal_windshield_wiper.gif?resize=700%2C400&ssl=1 2x"},"classes":[]}],"_links":{"self":[{"href":"https:\/\/www.allendowney.com\/blog\/wp-json\/wp\/v2\/posts\/1794","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.allendowney.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.allendowney.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.allendowney.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.allendowney.com\/blog\/wp-json\/wp\/v2\/comments?post=1794"}],"version-history":[{"count":2,"href":"https:\/\/www.allendowney.com\/blog\/wp-json\/wp\/v2\/posts\/1794\/revisions"}],"predecessor-version":[{"id":1799,"href":"https:\/\/www.allendowney.com\/blog\/wp-json\/wp\/v2\/posts\/1794\/revisions\/1799"}],"wp:attachment":[{"href":"https:\/\/www.allendowney.com\/blog\/wp-json\/wp\/v2\/media?parent=1794"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.allendowney.com\/blog\/wp-json\/wp\/v2\/categories?post=1794"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.allendowney.com\/blog\/wp-json\/wp\/v2\/tags?post=1794"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}