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Tech · reviewed 2026-05-16

What are the odds of problematic social-media use as an adult?

Evidence quality 4.38/5

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
3/5
D4 Uncertainty
4/5
D5 Scope
5/5
D6 Prose
5/5
D7 Perception honesty
4/5
D8 Caveat completeness
5/5
Average 4.38/5

Lifetime probability · lifetime, US adult

1 in 13

8.0% lifetime chance

range 1 in 25 to 1 in 6.7

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 4.2 1 in 13

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≈ As likely as

Abstract illustration of a phone with stacked notification badges, muted tones, flat vector illustration.

Perceived

Problematic social media use occupies an unusual position in the public risk landscape: it is simultaneously over-discussed in the media and under-estimated in terms of clinical prevalence. Parents, educators, and policymakers focus heavily on adolescent risk, often underweighting the proportion of adults who also meet problematic-use thresholds on validated scales. At the same time, the absence of a DSM-5 or ICD-11 diagnosis for social media addiction (in contrast to gaming disorder, which entered ICD-11 as 6C51 in 2022) creates widespread uncertainty about whether the phenomenon is real, exaggerated, or simply heavy use mislabeled as disorder. Popular discourse alternates between treating social media as mildly habit-forming and framing it as an existential crisis for mental health — both positions overshoot what the epidemiological data actually show.

Rough estimate: ~5-15% of adults

Source: editorial intuition, not polled

Actual

~5% of adults meet strict problematic social media use criteria on validated scales (pooled across representative studies; monothetic/strict classification)

adults across multiple countries (meta-analytic pooled estimate using strict/monothetic cut-off classifications)

Show derivation

The pooled prevalence of problematic social media use (PSU) using strict monothetic or severe cut-off criteria on validated scales (primarily the Bergen Social Media Addiction Scale, BSMAS) is approximately 5% (95% CI: 3%–7%) in representative adult samples, based on meta-analytic synthesis. Using moderate cut-off or polythetic criteria raises this to approximately 13-25%. We use the 5% strict-criteria figure as the native rate. For lifetime normalization, we apply a modest upward adjustment from the point-prevalence 5%: problematic social media use patterns are dynamic — individuals cycle in and out of problematic use over a lifetime, particularly as platforms and life circumstances change — so a larger share of adults will meet criteria at some point across a lifetime than at any single measurement. A lifetime_us_adult of 0.08 (8%) reflects a conservative 1.6x multiplier on the point prevalence, acknowledging that the cumulative lifetime fraction exceeds the cross-sectional rate. The uncertainty range (0.04–0.15) spans from a strict-criteria lower bound close to the point-prevalence floor to the moderate-criteria upper bound, given the substantial instrument-dependence of the estimate.

Caveats: Problematic social media use (PSU) is not listed in DSM-5 and does not appear in…

Problematic social media use (PSU) is not listed in DSM-5 and does not appear in ICD-11 as of 2026. Internet Gaming Disorder entered ICD-11 (6C51) in 2022; social media use did not receive analogous recognition, reflecting ongoing scientific debate about whether the evidence base meets the threshold for a formal disorder category. All prevalence estimates here are based on validated scale scores (primarily BSMAS), not clinical diagnoses. The prevalence estimate is extremely sensitive to the cut-off or classification scheme used: strict monothetic criteria yield approximately 5%, while moderate polythetic criteria yield approximately 25% in the same underlying data. The lifetime_us_adult figure (0.08) involves a cross-sectional-to-lifetime extrapolation for which no longitudinal data currently exist. BSMAS studies are predominantly from non-US populations and from younger adult samples; US-specific representative adult data are limited. The concept of "social media addiction" remains contested — some researchers argue that high use reflects platform design incentives rather than individual pathology, and that addiction framing may stigmatize ordinary behavior.

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Compare to:

Using strict monothetic criteria on validated scales — primarily the Bergen Social Media Addiction Scale (BSMAS) — approximately 5% of adults across studies meet the threshold for problematic social media use (PSU), based on a 2023 meta-analysis of 139 independent samples from 32 countries (n=133,955) published in Drug and Alcohol Dependence. That figure rises to roughly 13% using a severe cut-off and to 25% using moderate polythetic criteria, which is to say: the number produced depends almost entirely on where researchers choose to draw the line. In individualistic nations — a reasonable cultural proxy for the US — the meta-analytic range sits between 1.5% and 15%, bracketing the strict-criteria estimate from below and above. These are scale-positive prevalence figures, not clinical diagnoses; there is no DSM-5 or ICD-11 code for social media addiction, which makes any comparison with formally diagnosed disorders inexact.

The contrast with gaming disorder is instructive. Internet Gaming Disorder entered ICD-11 as code 6C51 in 2022, having accumulated sufficient clinical and epidemiological research to satisfy WHO’s criteria for disorder recognition. Social media use did not receive equivalent recognition in the same revision cycle — the evidence base was judged insufficient or the phenomenology too diffuse to specify. That distinction matters: without a diagnostic code, there is no systematic clinical surveillance, insurance reimbursement for treatment is difficult to arrange, and researchers lack a shared case definition. The BSMAS, developed by Andreassen and colleagues (2016), has become the de facto standard instrument, but cut-off scores remain debated and the scale was not designed as a clinical diagnostic tool.

The 8% lifetime estimate here applies the strict-criteria 5% cross-sectional figure with a conservative upward adjustment for the likelihood that more adults cross a PSU threshold at some point in a lifetime than at any given measurement — PSU patterns are dynamic and platform-dependent. That extrapolation carries real uncertainty, and there are no longitudinal US adult studies that would anchor it precisely. The US-specific evidence base is thin; most BSMAS data come from European, Asian, and Middle Eastern samples. The heavy-use patterns most predictive of PSU (passive scrolling, late-night use, use as primary mood regulation) correlate with depression and anxiety bidirectionally, making causal inference difficult from cross-sectional data. None of this settles the contested framing question — whether PSU reflects individual pathology, platform design optimization for engagement, or ordinary behavioral variation that scale-based measurement systematically medicalizes.

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] Drug and Alcohol Dependence / ScienceDirect — Has the prevalence of problematic social media use increased over the past seven years and since the start of the COVID-19 pandemic? A meta-analysis of the studies published since the development of the Bergen social media addiction scale
    Has the prevalence of problematic social media use increased over the past seven years and since the start of the COVID-19 pandemic? A meta-analysis of the studies published since the development of the Bergen social media addiction scale
    Statistic
    Pooled prevalence of problematic social media use: ~5% (95% CI 3%–7%) using strict/monothetic classifications; 13% using severe cut-off; 25% using moderate cut-off; 139 independent samples, 32 countries, n=133,955
    Excerpt
    “"The pooled prevalence estimate was 5% (95% CI: 3%–7%) for studies adopting monothetic or strict monothetic classifications, with a higher pooled prevalence estimate (13%; 95% CI: 8%–19%) found for studies adopting a cutoff for severe level or strict polythetic classifications, and 25% (95% CI: 21%–29%) for studies adopting a cutoff for moderate level or polythetic classifications. PSMU as assessed by the BSMAS was significantly higher in low-income countries." ”
    Source data from
    2023-08-01
    Accessed
    2026-05-04 · archived copy
    Calculation
    Primary prevalence source. The 5% strict-criteria figure is used as the native rate (numerator=5, denominator=100). The 139-sample meta-analysis (n=133,955) spanning 32 countries provides the most comprehensive synthesis of BSMAS-based PSU prevalence to date. For normalization to lifetime_us_adult=0.08, we apply a conservative upward adjustment from the cross-sectional 5% to account for the dynamic, cyclical nature of PSU over a lifetime (individuals enter and leave problematic use states). The 95% CI range from strict (3%–7%) to moderate (21%–29%) criteria bounds the uncertainty range; we use 4%–15% as the uncertainty bounds to reflect realistic variability in strict-to-moderate definitions for a US adult context.
  2. [2] Journal of Behavioral Addictions / PMC — Psychometric properties of the Bergen Social Media Addiction Scale: An analysis using item response theory
    Psychometric properties of the Bergen Social Media Addiction Scale: An analysis using item response theory
    Statistic
    The Bergen Social Media Addiction Scale (BSMAS) is a validated 6-item instrument for assessing problematic social media use; cut-off score of ≥19 (of 30) commonly used for at-risk designation
    Excerpt
    “"The Bergen Social Media Addiction Scale (BSMAS) is the most widely used instrument to assess problematic social media use (PSMU). Social media addiction is estimated to affect 13% to 25% of individuals globally, and given the significant prevalence of social media addiction estimated to affect 13% to 25% of individuals globally, validating reliable measures is of paramount importance." ”
    Source data from
    2022-12-01
    Accessed
    2026-05-04 · archived copy
    Calculation
    Supporting source establishing the psychometric properties of the BSMAS, the primary instrument underlying the meta-analytic estimates in the primary source. The 13%–25% global range cited here reflects moderate-to-severe cut-off criteria; the strict-criteria 5% figure from the 2023 meta-analysis is a subset of this broader range. This source establishes that BSMAS is not a clinical diagnostic instrument — it measures scale-positive problematic use, not a recognized DSM-5 or ICD-11 disorder.
  3. [3] Drug and Alcohol Dependence / PubMed — Prevalence of social media addiction across 32 nations: Meta-analysis with subgroup analysis of classification schemes and cultural values
    Prevalence of social media addiction across 32 nations: Meta-analysis with subgroup analysis of classification schemes and cultural values
    Statistic
    Pooled social media addiction prevalence: 24% globally (BSMAS mean-score method); 14% in individualistic nations; 31% in collectivistic cultures; 139 samples from 32 countries
    Excerpt
    “"The meta-analysis of thirty-two countries showed a pooled overall prevalence of 24% worldwide, comprised between 14% in individualistic nations and 31% in collectivistic cultures. Prevalence rates were lower in Western countries (1.5%–15%) compared to those found in Asia (31%) and the Middle East (29%)." ”
    Source data from
    2021-04-01
    Accessed
    2026-05-04 · archived copy
    Calculation
    Earlier meta-analysis (Cheng et al. 2021) using mean BSMAS scores rather than cut-off criteria. The 14% figure for individualistic nations (which better approximates the US context) provides an upper-bound anchor. The range across classification schemes (1.5%–31% within Western countries) illustrates the extreme instrument-dependence of PSU estimates. This source is used to contextualize the strict-criteria 5% native figure within the broader evidence base — demonstrating that the estimate is highly sensitive to measurement choice.

412 risks with measured probability
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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