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Other · reviewed 2026-05-16

What are the odds of developing compulsive buying disorder?

Evidence quality 4.5/5

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

D1 Source grounding
5/5
D2 Source authority
5/5
D3 Arithmetic
4/5
D4 Uncertainty
5/5
D5 Scope
3/5
D6 Prose
5/5
D7 Perception honesty
4/5
D8 Caveat completeness
5/5
Average 4.5/5
Direct evidence

Lifetime probability · lifetime, US adult

1 in 20

4.9% lifetime chance

Most people underestimate this.

range 1 in 29 to 1 in 14

lifetime, US adult each band = 10× rarer → zoomed to your factors See full scale →
certain 1 in 1K 1 in 1M 1 in 1B
1 in 6.8 1 in 20

● your factors — click this risk ▾ to reveal

≈ As likely as

Abstract illustration of stacked shopping bags casting a long shadow over a receipt, muted tones, flat vector.

Perceived

Compulsive buying is frequently dismissed as a wealthy-world quirk or a character flaw dressed up as a disorder. The popular concept of "retail therapy" frames occasional excessive shopping as harmless emotional regulation, and the explosion of online commerce — one-click purchasing, algorithmic recommendation, free returns — has normalized behaviors that would once have required more deliberate effort. Neither DSM-5 nor ICD-11 currently lists compulsive buying disorder as a standalone diagnosis, which contributes to both clinical underdetection and public underestimation of its prevalence. When the condition is acknowledged, it is often stereotyped as a women's problem or a mild impulse-control quirk, understating the financial destruction and psychiatric co-morbidity that characterize clinically significant cases.

Rough estimate: ~1-2% of adults

Source: editorial intuition, not polled

Actual

~4.9% pooled prevalence in adult representative populations (Maraz, Griffiths & Demetrovics, 2016, Addiction; meta-analysis of 40 studies)

adults in representative population samples across 16 countries (meta-analysis of 40 studies, n=32,000+)

Show derivation

Maraz, Griffiths & Demetrovics (2016, Addiction) conducted a systematic review and meta-analysis of 40 studies reporting 49 prevalence estimates from 16 countries (total n=32,000+). In adult representative population samples specifically, the pooled prevalence was 4.9% (95% CI: 3.4%–6.9%). This is treated as the best available estimate for the lifetime probability for a US adult, as no US-specific lifetime longitudinal study exists. The global pooled estimate is applied to the US adult context; the absence of strong evidence for major US-specific deviation supports this approximation. The CI from the meta-analysis (3.4%–6.9%) is used directly as the uncertainty range; the central estimate (0.049) sits within this range. Point prevalence is used here because no cumulative lifetime incidence studies exist for compulsive buying disorder; lifetime risk is plausibly somewhat higher than the cross-sectional 4.9%, but the meta-analytic pooled figure is the most rigorous available anchor.

Caveats: Compulsive buying disorder is not listed in DSM-5 or ICD-11 as a standalone diag…

Compulsive buying disorder is not listed in DSM-5 or ICD-11 as a standalone diagnosis as of 2026. Prevalence estimates vary substantially by measurement instrument: the Compulsive Buying Scale (CBS), the Edwards Compulsive Buying Scale, and the Questionnaire About Buying Behavior produce different cut-off rates. The Maraz et al. meta-analysis pools estimates from 2016 data — prior to the full expansion of mobile commerce, algorithmic recommendation engines, and one-click purchasing, all of which have likely increased prevalence since the meta-analysis was conducted. Female predominance is consistent across clinical samples but may partly reflect differential help-seeking and social acceptability of disclosing shopping problems. The 4.9% figure comes from representative adult populations; university and shopping-specific samples show much higher rates (8.3% and 16.2% respectively), indicating that context and sampling frame substantially affect estimates. No long-term longitudinal study of cumulative lifetime incidence exists.

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Compare to:

Approximately 4.9% of adults in representative population samples meet criteria for compulsive buying behavior on validated screening instruments, according to a 2016 systematic review and meta-analysis of 40 studies across 16 countries (total n=32,000+) by Maraz, Griffiths, and Demetrovics, published in Addiction (95% CI: 3.4%–6.9%). That places the disorder in roughly the same prevalence band as obsessive-compulsive disorder — common enough to appear regularly in any primary care caseload, rare enough that most people encountering it would not recognize it by name. The disorder is characterized by preoccupation with purchasing, recurrent unsuccessful attempts to curtail buying, and significant financial or relational consequences — distinct from ordinary impulsive spending by its persistence, distress, and functional impairment.

Neither DSM-5 nor ICD-11 currently classifies compulsive buying disorder as a standalone diagnosis. That absence is consequential: without a diagnostic code, insurance reimbursement for treatment is difficult, population surveillance is patchy, and clinicians may not screen for it routinely. The disorder sits in an awkward definitional space, variously proposed as an impulse-control disorder, an obsessive-compulsive spectrum condition, or a behavioral addiction — and the classification matters for which treatment models are applied. The 2016 meta-analysis predates the mobile commerce era; a 2016 smartphone user and a 2026 smartphone user experience purchasing friction that differs by an order of magnitude, and the evidence base has not yet caught up with what that shift means for compulsive buying prevalence.

The 4.9% estimate applies to representative adult samples; rates in university populations (8.3%) and shopping-specific samples (16.2%) are substantially higher, indicating that sampling frame dramatically shapes the estimate. Female predominance is consistent across clinical studies — women account for roughly 80% of clinical cases — but may partly reflect differential help-seeking or social acceptability of disclosure rather than true incidence differences. Co-occurring depression, anxiety, and OCD are common; the directional relationship (does buying behavior drive mood dysregulation, or vice versa) remains contested. There are no long-term longitudinal studies of cumulative lifetime incidence, so the prevalence figure here represents the proportion meeting criteria at a given cross-sectional measurement, not a true lifetime probability.

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] Addiction / PubMed — The prevalence of compulsive buying: a meta-analysis
    The prevalence of compulsive buying: a meta-analysis
    Statistic
    Pooled prevalence of compulsive buying in adult representative populations: 4.9% (95% CI: 3.4%–6.9%); 40 studies, 16 countries, n>32,000
    Excerpt
    “"The pooled prevalence for compulsive buying behaviour in adult representative samples was 4.9% (95% CI 3.4–6.9%), compared with 12.3% in adult non-representative samples, 8.3% in university student populations, and 16.2% in shopping-specific samples." ”
    Source data from
    2016-03-01
    Accessed
    2026-05-04 · archived copy
    Calculation
    Primary prevalence source. The 4.9% figure (95% CI 3.4%–6.9%) from representative adult population samples is used directly as the native rate (numerator=4.9, denominator=100). For normalization, lifetime_us_adult=0.049 treats this global pooled cross-sectional rate as a US-adult approximation, with the meta-analytic 95% CI providing the uncertainty bounds directly (low=0.034, high=0.069). The Maraz et al. meta-analysis pooled studies using the Compulsive Buying Scale (CBS), the Compulsive Buying Screening Tool (CBST), the Questionnaire About Buying Behavior (QABB), and other validated instruments.
  2. [2] Addiction / Wiley Online Library — The prevalence of compulsive buying: a meta-analysis — Addiction (Wiley)
    The prevalence of compulsive buying: a meta-analysis — Addiction (Wiley)
    Statistic
    Meta-analysis of 40 studies, pooled prevalence 4.9% in representative adult samples (95% CI 3.4%–6.9%)
    Excerpt
    “"The meta-analysis found that the pooled prevalence for compulsive buying behaviour in adult representative population samples was 4.9% (95% CI: 3.4–6.9), with significant between-study heterogeneity." ”
    Source data from
    2016-03-01
    Accessed
    2026-05-04 · archived copy
    Calculation
    Secondary citation to the Wiley journal version of the same Maraz et al. (2016) meta-analysis. Confirms the 4.9% (95% CI 3.4%–6.9%) finding in representative adult samples. No additional arithmetic beyond the primary source.
  3. [3] Journal of Behavioral Addictions / PMC — Treatments for compulsive buying: A systematic review of the quality, effectiveness and progression of the outcome evidence
    Treatments for compulsive buying: A systematic review of the quality, effectiveness and progression of the outcome evidence
    Statistic
    Compulsive buying disorder affects an estimated 5% of the general adult population; not currently listed in DSM-5 or ICD-11 as a standalone diagnosis
    Excerpt
    “"Compulsive buying disorder (CBD) affects an estimated 5% of the general adult population. Despite its prevalence, CBD is not currently listed in the DSM-5 or ICD-11 as a standalone diagnosis, and no pharmacological treatment has been approved. Cognitive behavioral therapy remains the most studied psychotherapeutic approach." ”
    Source data from
    2017-01-01
    Accessed
    2026-05-04 · archived copy
    Calculation
    Supporting source confirming the approximately 5% prevalence figure and the absence of a formal DSM-5/ICD-11 diagnosis. This source also documents the treatment evidence gap, which is relevant context for the caveats section. Consistent with the Maraz et al. meta-analytic estimate of 4.9%.

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