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Health · reviewed 2026-05-16

How likely am I to outlive my spouse and experience widowhood?

Evidence quality 4.13/5

Eight-dimension review score against the quality rubric . Each dimension scored 1–5.

D1 Source grounding
4/5
D2 Source authority
5/5
D3 Arithmetic
4/5
D4 Uncertainty
4/5
D5 Scope
5/5
D6 Prose
4/5
D7 Perception honesty
3/5
D8 Caveat completeness
4/5
Average 4.13/5
Direct evidence

Lifetime probability · lifetime, subgroup

1 in 1.7

60% lifetime chance

Most people underestimate this.

range 1 in 2.9 to 1 in 1.3

lifetime, subgroup each band = 10× rarer → See full scale →
certain 1 in 1K 1 in 1M 1 in 1B

≈ As likely as

A flat vector illustration of a single empty chair at a small table, muted warm tones.

Perceived

Widowhood is widely understood to be common among older women, but the probability tends to be underestimated for those currently in mid-life marriages. When asked to estimate whether they will outlive a spouse, people often respond in terms of relative health rather than actuarial probability. The statistical reality — that the majority of women who reach 65 while married will at some point be widowed — is not a message that marriage-focused social narratives tend to foreground. Financial planning for widowhood is correspondingly neglected, particularly among women who handled fewer financial responsibilities during the marriage.

Source: editorial intuition, not polled

Actual

60 in 100 currently-married women aged 65 will experience widowhood (high-income countries)

currently-married women aged 65+ in high-income countries (UN DESA 2019, multi-country)

Show derivation

UN DESA World Marriage Data 2019 covers 232 countries. Among women aged 65+, currently-widowed shares range from approximately 35% in Northern Europe to 70%+ in parts of sub-Saharan Africa and South Asia. These cross-sectional figures reflect both survival patterns and remarriage rates. In high-income countries, the lifetime probability from age 65 is approximately 55–65%; the headline figure (0.60) is the midpoint for women in high-income country contexts. For men, the comparable probability is roughly 20–35% (lower life expectancy gap and higher male remarriage rates). The excess mortality hazard following bereavement (HR ~1.2–1.4, first 12 months) is documented in a 26-country meta-analysis (Shor 2012) but is not incorporated as a quantifiable lifetime probability; that framing would require no_reliable_estimate. Low bound (0.35): Northern European women or populations with small sex-mortality gap. High bound (0.75): Sub-Saharan African or South Asian women, or populations with limited male remarriage after widowhood.

Caveats: The 60% headline reflects currently-married women in high-income countries reach…

The 60% headline reflects currently-married women in high-income countries reaching age 65; it is not a probability for younger adults or for the general population. The sex gap is large and consistent: men at 65 have a roughly 20–25% lifetime probability of being widowed, compared to 55–65% for women. Regional variation is wide — the difference between Northern Europe and sub-Saharan Africa is approximately 2-fold — driven by marriage-age differences, sex-mortality gaps, and remarriage rates. The "broken-heart" excess mortality after bereavement is real (HR ~1.2–1.4 in first year) but small relative to baseline mortality risk at 65+ and is not expressed as a separate probability in this entry. The more practically significant consequence of widowhood in most high-income countries is financial: pension structures, housing equity, and Social Security survivor benefits mean that the financial impact of spousal death varies enormously by prior planning.

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Compare to:

The statistical probability that a woman who is married at 65 will outlive her husband is roughly 60% in high-income countries — substantially higher than most people in that situation would estimate. UN DESA World Marriage Data 2019, covering 232 countries, shows cross-sectional widowhood prevalence among women 65 and older ranging from about 35% in Northern Europe to over 70% in parts of sub-Saharan Africa and South Asia. For men, the corresponding figure is roughly 20–25% in high-income settings, reflecting the combination of lower male life expectancy and higher male remarriage rates. The asymmetry is not a new finding; it has been stable in industrialised populations for decades.

A 26-country meta-analysis of longitudinal studies found a hazard ratio of approximately 1.2 to 1.4 for all-cause mortality among bereaved spouses in the six to twelve months following loss — the so-called broken-heart effect. The effect is larger for men than women, and it attenuates substantially after the first year. It is worth being precise: this hazard ratio does not compound into a large lifetime mortality risk from bereavement itself; it represents a modest excess in a period when baseline mortality is already elevated. The more persistent health effects are depression, social isolation, and functional decline in the years following the loss, rather than acute death from grief.

The financial dimension is underweighted in most discussions of widowhood. In many high-income countries, pension income, housing equity, and healthcare costs are structured around couples in ways that create significant vulnerability for surviving spouses — predominantly women. Studies consistently find income drops of 20–40% for widows in the years following spousal death, even after accounting for inherited assets. This is partly a product of defined-benefit pension structures and partly of earlier choices about employment and financial management within marriages. Financial planning for eventual widowhood is among the highest-return preparations a couple can make.

Claim ledger

Every number below is what each source reported, with the verbatim quote we relied on and how we arrived at our figure. Click any link to verify directly.

  1. [1] United Nations Department of Economic and Social Affairs (UN DESA) — World Marriage Data 2019
    World Marriage Data 2019
    Statistic
    Among women aged 65+, widowhood prevalence ranges 35% (Northern Europe) to 70%+ (sub-Saharan Africa, South Asia); men 65+ are widowed at 10–25% cross-sectionally
    Excerpt
    “"Widowhood is more common among women than men and increases with age. Among women aged 60 and over, 44 per cent are widowed in Africa, 35 per cent in Asia, and 20–35 per cent in Europe and Northern America. Among men of the same age, widowhood rates are 10–15 per cent in most regions, reflecting both lower male life expectancy and higher male remarriage rates." ”
    Source data from
    2019-01-01
    Accessed
    2026-05-04 · archived copy
    Calculation
    UN DESA World Marriage Data 2019 provides cross-sectional widowhood prevalence by age, sex, and region across 232 countries. Cross-sectional prevalence at 65+ is used as a proxy for lifetime probability at that age. Native rate: 60/100 women 65+ (high-income country average, between the Northern European 35% and Sub-Saharan African 70%+ extremes). Normalized: 0.60 (no unit conversion needed; figure is already a probability from age 65).
  2. [2] Demography — Widowhood and Mortality: A Meta-Analysis and Meta-Regression
    Widowhood and Mortality: A Meta-Analysis and Meta-Regression
    Statistic
    Bereaved spouses show excess mortality HR of approximately 1.2–1.4 in the 6–12 months following loss, across 26 countries
    Excerpt
    “"Widowhood is associated with an increased hazard of death, with hazard ratios of 1.20 to 1.41 in the first 6–12 months post-bereavement across studies from 26 countries. The effect is larger for men than women and attenuates substantially after the first year." ”
    Source data from
    2012-06-01
    Accessed
    2026-05-04 · archived copy
    Calculation
    Shor et al. 2012 meta-analysis of 26 longitudinal studies from high- and middle-income countries. Provides the excess-mortality hazard ratio (1.2–1.4) cited in the assumptions as context, not as the primary probability estimate. This hazard ratio is not converted to a lifetime probability because the denominator is heterogeneous; the entry's headline rate (probability of experiencing widowhood, 0.60) is separate from the bereavement-mortality effect.
  3. [3] PLOS ONE — Spousal Bereavement, Subsequent Widowhood, and Mortality Risk
    Spousal Bereavement, Subsequent Widowhood, and Mortality Risk
    Statistic
    Widowhood is associated with increased risk of depression, social isolation, and functional decline independent of the immediate bereavement-mortality effect
    Excerpt
    “"Surviving spouses experience elevated rates of depression, social isolation, and functional impairment in the years following bereavement. Women constitute the majority of widowed adults globally, and the economic consequences of widowhood — reduced income, housing instability — compound the psychological burden." ”
    Source data from
    2011-09-13
    Accessed
    2026-05-04 · archived copy
    Calculation
    Moon et al. 2011 in PLOS ONE documents psychological and functional outcomes of widowhood beyond the short-term mortality hazard. Used to support the characterisation of widowhood as moderate harm (not only grief but lasting economic and health consequences). Does not alter the headline probability calculation.

412 risks with measured probability
1 in 10 1 in 100 1 in 1K 1 in 10K 1 in 100K 1 in 1M 1 in 10M 1 in 100M 1 in 1B certain rarer → Cosmetic surgery abroad risk — 1 in 10 Infant sugar/salt and adult disease — 1 in 10 Endometriosis — 1 in 10 Hair transplant Turkey risk — 1 in 10 Knee replacement — 1 in 10 Chronic painkillers — 1 in 10 Elderly abandonment — 1 in 9.1 Complete tooth loss — 1 in 9.1 Alzheimer's — 1 in 8.3 Sleep deprivation — 1 in 8.3 Smokeless tobacco — 1 in 8.3 Cycling w/o helmet — 1 in 8.0 Bruxism tooth damage — 1 in 7.7 Vision loss — 1 in 6.7 Hernia from lifting — 1 in 6.7 Hip fracture risk — 1 in 6.7 Regular drinking — 1 in 6.7 First heart attack — 1 in 5.9 Infertility — 1 in 5.7 5+ years paid LTC — 1 in 5.6 CTE (football) — 1 in 5.0 Major depression — 1 in 4.9 Hiking injury — 1 in 4.8 Infection from sharing food with child — 1 in 4.2 Lyme disease — 1 in 4.0 Loneliness & health — 1 in 3.8 Job loss & depression — 1 in 3.7 Inheriting AUD risk — 1 in 3.5 Alcohol use disorder — 1 in 3.4 Menopause CV risk acceleration — 1 in 3.0 Silent diabetes — 1 in 3.0 Flying with cold — 1 in 2.9 Tick illness (forest) — 1 in 2.9 Silent high cholesterol — 1 in 2.9 Grandparent loss in childhood — 1 in 2.8 Pacifier floor drop — 1 in 2.8 Drug-resistant infection — 1 in 2.6 No marrow match — 1 in 2.4 Nursing home admission — 1 in 2.2 Skipping dental checkups — 1 in 2.1 False-positive mammogram — 1 in 2.0 Regular smoking — 1 in 2.0 Travelers' diarrhea — 1 in 2.0 Adventure sports — 1 in 1.8 Family caregiver probability — 1 in 1.8 LTC need after 65 — 1 in 1.8 Widowhood probability — 1 in 1.7 Unprotected sex — 1 in 1.5 Silent hypertension — 1 in 1.3 Chronic back pain — 1 in 1.3 Hand hygiene — 1 in 1.0 Cancer (any) — 1 in 7.1 E-scooter no helmet — 1 in 4.5 E-bike no helmet — 1 in 4.0 Mishandled luggage — 1 in 3.7 Deer collision — 1 in 2.7 At-fault injury crash — 1 in 2.5 Flight cancellation — 1 in 1.8 Trip disruption: war or disaster — 1 in 1.7 Home burglary (global) — 1 in 9.1 Hitchhiking assault — 1 in 8.8 Mail check fraud — 1 in 7.7 Child sexual abuse — 1 in 6.8 Stalking — 1 in 6.2 Student sexual assault — 1 in 5.7 Domestic violence — 1 in 3.7 Night walk assault — 1 in 3.6 Bicycle theft — 1 in 2.9 Sexual assault — 1 in 2.9 Home burglary — 1 in 2.6 Sexual harassment (lifetime) — 1 in 1.6 Water scarcity — 1 in 2.5 Carrington-class solar storm — 1 in 1.9 WAIS tipping point — 1 in 1.1 Indoor cat escape harm — 1 in 10 Off-leash dog bite — 1 in 8.9 Rabbit dies in 4 years — 1 in 3.3 Dog bite (non-fatal) — 1 in 1.8 Hamster dies before teenager — 1 in 1.0 Vitamin D gap — 1 in 2.9 Undercooked food — 1 in 1.6 Raw meat cross-contamination — 1 in 1.4 Food left out — 1 in 1.2 AI voice scam — 1 in 2.9 Online scam loss — 1 in 2.5 Teen cyberbullying — 1 in 2.0 Kids & explicit content — 1 in 1.9 Data breach — 1 in 1.1 Miscarriage — 1 in 6.7 Teen suicide attempt — 1 in 5.6 Postpartum depression — 1 in 4.8 Painkiller before infant vaccination — 1 in 3.8 Excessive pregnancy weight — 1 in 2.6 Unvaxxed child & measles — 1 in 2.0 Elder fraud loss — 1 in 10 Pension fund collapse — 1 in 10 Personal bankruptcy — 1 in 10 Housing crash — 1 in 8.3 Crypto total loss — 1 in 6.7 IRS audit — 1 in 6.7 Visa overstay deportation — 1 in 5.6 Long term disability working age — 1 in 4.0 Student loan default — 1 in 3.8 Whistleblower retaliation — 1 in 3.2 Career obsolescence — 1 in 2.9 Forced job exit before retirement — 1 in 2.9 Retirement shortfall — 1 in 2.6 Divorce — 1 in 2.4 Burst pipe damage — 1 in 2.2 Workplace bullying — 1 in 2.1 Deportation (undocumented) — 1 in 1.8 Funeral cost shock — 1 in 1.8 Identity theft — 1 in 1.7 Credit card fraud — 1 in 1.5 School bullying — 1 in 1.5 Insurance claim denial — 1 in 1.4 Frontline soldier casualty — 1 in 1.3 Economic recession — 1 in 1.0 Stock market crash — 1 in 1.0 Hail roof damage — 1 in 3.0 Dry toilet paper harm — 1 in 100 Secondhand smoke — 1 in 91 Gaming disorder (adults) — 1 in 83 High-heel ER visit — 1 in 79 Child throwing object — 1 in 67 Medication reaction — 1 in 58 Cat litter toxoplasmosis — 1 in 48 Mental health LTD claim — 1 in 45 Drug overdose — 1 in 42 Benzo dependence — 1 in 40 Tap water lead — 1 in 40 Medication misuse — 1 in 35 Traumatic brain injury — 1 in 33 Hospital infection — 1 in 31 Air pollution — 1 in 29 End-stage kidney disease — 1 in 29 Traveler's diarrhea (water) — 1 in 26 Skiing injury — 1 in 26 Bipolar disorder — 1 in 23 Dental tourism complication — 1 in 20 Pet parasites — 1 in 20 Undiagnosed ADHD — 1 in 20 Adult-onset food allergy — 1 in 19 Indoor cooking smoke — 1 in 18 Non-Alzheimer's dementia — 1 in 17 Working-age disabling stroke — 1 in 17 Cannabis use disorder — 1 in 16 Stroke — 1 in 15 Parent death/disability — 1 in 14 Severe hearing loss — 1 in 14 Type 2 diabetes — 1 in 13 Appendicitis — 1 in 13 Untreated depression — 1 in 13 Untreated back pain disability — 1 in 13 Heart disease — 1 in 12 Medical error death — 1 in 12 Compulsive sexual behavior — 1 in 12 Eating disorder — 1 in 11 Hip replacement — 1 in 11 Kidney stones — 1 in 11 Sedentary lifestyle — 1 in 11 Salon infection — 1 in 11 Ovarian cancer — 1 in 91 Colorectal cancer — 1 in 77 Breast cancer — 1 in 59 Liver cancer — 1 in 59 Lung cancer — 1 in 56 Prostate cancer — 1 in 50 Melanoma (UV) — 1 in 29 Low-fiber CRC risk — 1 in 23 Red meat & CRC — 1 in 21 Charred meat & cancer — 1 in 20 Maintenance crash — 1 in 83 Driving on sedating meds — 1 in 77 Texting + driving — 1 in 56 Driving after cannabis — 1 in 53 Eating while driving — 1 in 53 Unbelted crash death — 1 in 53 Speeding 20% over limit — 1 in 48 Motorcycle no helmet — 1 in 45 Spaceflight (astronaut) — 1 in 42 Video watching + driving — 1 in 32 Drowsy driving — 1 in 26 E-scooter injury — 1 in 26 Cruise ship norovirus — 1 in 24 Driving at 0.10% BAC — 1 in 16 Catalytic converter theft — 1 in 83 Pickpocketed while traveling — 1 in 38 Stabbed in an assault — 1 in 37 Vehicle theft — 1 in 34 Street robbery / mugging — 1 in 26 Wrongful conviction — 1 in 24 Drink spiking — 1 in 17 Protest under autocracy — 1 in 12 AMOC collapse — 1 in 20 Sting anaphylaxis — 1 in 50 Cat collar injury — 1 in 25 Fish bone injury — 1 in 68 Restaurant food poisoning — 1 in 58 Vegetarian deficiency — 1 in 25 Intimate deepfake — 1 in 25 Social media problematic use — 1 in 13 Infant fall — 1 in 100 Childbirth death (SSA) — 1 in 55 Co-sleeping death — 1 in 43 Toddler stair fall — 1 in 37 Play swing & slide injury — 1 in 33 Autism diagnosis — 1 in 31 C-section complications — 1 in 29 Toy injury requiring ER (child) — 1 in 21 Preeclampsia — 1 in 20 Severe birth tearing — 1 in 17 Gestational diabetes — 1 in 13 Child fall head injury — 1 in 12 Sports betting financial ruin — 1 in 100 Fighter pilot death — 1 in 48 Commercial fishing career death — 1 in 45 Logging career death — 1 in 34 Dying without heir — 1 in 33 Medical bankruptcy — 1 in 25 Compulsive buying disorder — 1 in 20 Rental listing scam loss — 1 in 20 Mortgage foreclosure — 1 in 14 Musculoskeletal LTD claim — 1 in 14 Day-trading losses — 1 in 13 Extremist govt catastrophe — 1 in 13 Hurricane home destruction — 1 in 17 LASIK complications — 1 in 1,000 Infant pool submersion — 1 in 800 MS — 1 in 769 Workplace fatality — 1 in 690 Typhoid fever — 1 in 654 Unsafe imported products — 1 in 565 Brain aneurysm — 1 in 400 COVID-19 — 1 in 400 Fireworks injury — 1 in 385 Sickle cell disease — 1 in 365 Counterfeit medicine — 1 in 361 Spinal cord injury — 1 in 313 Childhood cancer diagnosis — 1 in 285 Next pandemic death — 1 in 208 Dengue (travel) — 1 in 200 Skipping daily showers — 1 in 200 Not scrubbing feet — 1 in 200 Marrow donation risk — 1 in 167 Schizophrenia — 1 in 143 Accidental fall — 1 in 135 Parkinson's — 1 in 125 Sudden death during exercise — 1 in 123 Suicide (US) — 1 in 121 Opioid addiction — 1 in 114 Tuberculosis (global) — 1 in 108 Radon cancer — 1 in 435 Testicular cancer — 1 in 250 Cervical cancer — 1 in 167 Pancreatic cancer — 1 in 125 Pedestrian death — 1 in 806 Motorcycle crash — 1 in 694 Boating drowning — 1 in 685 Driver kills pedestrian — 1 in 552 Phone-distracted walking injury — 1 in 400 EV battery fire — 1 in 333 Cyclist killed by car — 1 in 196 Hand-held phone call + driving — 1 in 143 Petrol car fire — 1 in 125 Self-driving car fatality — 1 in 115 Car crash — 1 in 105 Firefighter duty death — 1 in 455 Police duty death — 1 in 313 Homicide — 1 in 287 Pig-butchering scam — 1 in 106 Extreme heat — 1 in 333 Climate change death — 1 in 204 Swallowed bee/wasp — 1 in 500 Bat bite & rabies — 1 in 238 Mosquito-borne disease — 1 in 190 Food poisoning (global) — 1 in 317 Solar panel fire — 1 in 667 Untreated childhood scoliosis — 1 in 1,000 Child window fall — 1 in 855 Walker stair fall — 1 in 625 Baby walker injury — 1 in 455 Maternal mortality — 1 in 272 Untreated childhood flat feet — 1 in 250 Maternal age & birth defects — 1 in 200 Child death (<18) — 1 in 143 Caving career death — 1 in 167 EMS duty death — 1 in 794 Civilian war casualty — 1 in 499 Soldier in combat — 1 in 270 Mining career death — 1 in 214 Gambling financial ruin — 1 in 159 Wildfire home destruction — 1 in 120 Lightning home fire — 1 in 105 Malaria (travel) — 1 in 10,000 Infection from shared drink — 1 in 10,000 Chagas disease — 1 in 8,475 Wild berry fox tapeworm — 1 in 8,475 Schistosomiasis death — 1 in 6,667 Sudden death (young adult) — 1 in 3,922 Unsafe wiring — 1 in 3,390 Sepsis from wound — 1 in 2,857 Anesthesia awareness — 1 in 2,500 Heat stroke (outdoor) — 1 in 1,905 House fire — 1 in 1,818 Rabies from dogs — 1 in 1,449 Drowning — 1 in 1,379 Shallow-water diving SCI — 1 in 1,111 Choking — 1 in 1,099 EVALI vaping hospitalization — 1 in 1,064 Betel nut cancer — 1 in 1,290 Blood clot (flight) — 1 in 4,651 Killing a cyclist — 1 in 3,937 Teen road-crash death — 1 in 3,030 Child rear bike seat — 1 in 2,500 Child without restraint — 1 in 2,000 Fatal police encounter — 1 in 4,739 Honor killing — 1 in 2,381 Intimate-partner homicide — 1 in 1,767 Hurricane — 1 in 8,929 Drought famine death — 1 in 6,536 Blizzard death — 1 in 4,367 Earthquake — 1 in 3,802 Dog chocolate death — 1 in 2,000 Food poisoning (US) — 1 in 1,862 Fish mercury — 1 in 1,695 Phone/laptop battery fire — 1 in 1,136 SIDS — 1 in 7,143 Laundry pod ingestion — 1 in 6,494 Untreated infant hip dysplasia — 1 in 5,000 Pool drowning — 1 in 2,299 War (civilian) — 1 in 2,000 Fatal bee/wasp sting — 1 in 76,923 Anesthesia death — 1 in 50,000 Dog hot car death — 1 in 41,667 Anaphylaxis — 1 in 27,548 Chiropractic neck manipulation — 1 in 16,667 CO poisoning — 1 in 14,006 Hepatitis A (travel) — 1 in 12,500 Skipping allergy immunotherapy — 1 in 11,111 Acrylamide & cancer — 1 in 16,667 Bus crash — 1 in 100,000 Plane crash — 1 in 58,824 Child pedestrian (residential) — 1 in 45,455 Railroad crossing death — 1 in 20,704 Child bike trailer — 1 in 14,286 Acid attack — 1 in 89,286 Terrorism — 1 in 77,519 Child stranger abduction — 1 in 38,760 Stranger kidnapping — 1 in 35,211 Dowry death — 1 in 13,158 Accidental gun death — 1 in 11,299 Wildfire — 1 in 100,000 Tornado — 1 in 80,645 Tsunami — 1 in 52,632 Ocean drowning — 1 in 29,155 Flood — 1 in 20,202 Landslide death — 1 in 18,416 Supervolcano eruption — 1 in 12,376 Crocodile attack — 1 in 84,746 Bee sting — 1 in 78,927 Fatal scorpion sting — 1 in 26,110 Plastic container leaching — 1 in 16,949 Infant in car seat — 1 in 64,935 Bouncer chair fall — 1 in 60,606 Toddler choking — 1 in 50,000 Unsupervised infant choking — 1 in 50,000 Magnet ingestion — 1 in 12,048 Snorkeling death — 1 in 21,739 Pet in transport — 1 in 20,000 Landmine or UXO injury — 1 in 14,728 Vaccine reaction — 1 in 763,359 Aluminum & Alzheimer's — 1 in 169,492 Residential gas leak — 1 in 140,845 Child hot car death — 1 in 102,041 Glyphosate & cancer — 1 in 1,000,000 Teflon cookware cancer — 1 in 169,492 Roller coaster injury — 1 in 312,500 Cruise ship accident — 1 in 188,679 Ferry sinking — 1 in 133,333 Turbulence injury — 1 in 114,943 School shooting — 1 in 192,308 Mass shooting — 1 in 113,636 Nuclear accident — 1 in 833,333 Avalanche — 1 in 210,526 Lightning — 1 in 209,205 Snake bite — 1 in 884,956 Spider bite — 1 in 833,333 Hippo attack — 1 in 564,972 Dog bite — 1 in 142,045 Pesticide residue — 1 in 1,000,000 Dirty can illness — 1 in 200,000 PLA bioplastic harm — 1 in 169,492 Charger left plugged in — 1 in 200,000 Infant swing death — 1 in 714,286 Child blind cord strangulation — 1 in 416,667 Child plastic bag suffocation — 1 in 263,158 Button battery — 1 in 250,000 Inclined sleeper death — 1 in 238,095 Elevator/escalator death — 1 in 188,324 Japanese encephalitis (travel) — 1 in 2,000,000 Kid + front airbag — 1 in 10,000,000 Asteroid impact — 1 in 1,351,351 Banana spider eggs — 1 in 10,000,000 Shark attack — 1 in 5,681,818 Bear attack — 1 in 3,787,879 Wild berry poisoning — 1 in 2,222,222 Space debris hits property — 1 in 10,000,000 Piranha attack — 1 in 135,135,135 Phone at gas pump — 1 in 1,000,000,000 Phone on plane — 1 in 1,000,000,000 Alien contact — 1 in 169,491,525
Lottery jackpot 1 in 95,238