Key evidence an nummers
| Group in the mobile study | Mean WPM | Context |
|---|---|---|
| The hale sample | 36.17 | SD 13.22; English transcription |
| Ages 10–19 | 39.6 | Age-group comparison, no change ower time |
| Ages 20–29 | 36.5 | Same study |
| Ages 40–49 | 28.9 | Same study |
| Ages 50–59 | 26.3 | Same study |
The overall values are gien in the results an Table 3; the age values are in Table 4. The sample's mean age wis 24.1 years, an folk war recruitit online. Younger volunteers an fowk comfortable wi English ty-pin war weel representit, sae dinna uise the table tae label ilka body o a gien age slow or fast. Palin et al., 2019
Is a phone ay slower nor a keyboard?
The authors compared their mobile results wi a muckle desktop study uisin a related method. The desktop study's mean wis 51.56 WPM. That is uissfu context, but it isna a randomised comparison o ilka participant on baith devices. Subtractin the means disna predict hoo muckle faster ye yersel wad ty-pe efter switchin. Palin et al., 2019 Dhakal et al., 2018
Ilkday techniques differ an aw. Motion-capture research shawes that self-taught physical-keyboard users can dae weel athoot follaein ae prescribed finger pattern. Mobile entry adds touchscreen targetin an helpin features, sae the desktop ten-finger rule isna a richt phone benchmark. Feit et al., 2016 MacKenzie and Soukoreff, 2002
Whit dis the error percentage mean?
The 2.34% value is aboot errors left in the transcribed output, uisin the study's measure. It isna the share o aa touches that war wrang, acause some errors war correctit. Text-entry methods distinguish final errors fae the effort spent correctin errors, an baith shoudna be hid by reportin speed alane. Soukoreff et al., 2003 Soukoreff et al., 2001
Whit affects it?
Age, sex an leid effects need context
The hale mobile sample's female an male means war 36.0 an 36.1 WPM, respectively, tho there wis a statistical difference in the muckle dataset. The practical difference atween thae hale-sample means is wee. The U.S. subsample shawed a different pattern, showin why a result's significance an hoo weel it applies mair widely are twa sindry questions. Palin et al., 2019
Native English users averaged 37.8 WPM in the same table. That link is nae proof that chyngin a phone's leid settin causes a specific improvement. The task needit English transcription, an familiarity can affect readin an enterin the text gien. Palin et al., 2019 Salthouse, 1986
Dae autocorrect an word prediction help?
In this dataset, uisin autocorrection wis positively associatit wi speed, while choosin predictit wirds wis negatively associatit. The study wis observational, sae thae links dinna prove that turnin on ae feature will cause the same improvement for ilka user. Fowk chuse features for sindry reasons, includin their existin skill an error patterns. Palin et al., 2019
For a uissfu personal comparison, keep the keyboard layout, helpin settins, leid an task type the same. Record correctness as weel as WPM. A message conversation, a memorised sentence an an unfamiliar transcription passage measure different mixes o makin language an motor entry. MacKenzie and Soukoreff, 2002 Soukoreff et al., 2003
Practice can improve skill at a particular task, but research on ilkday ty-pin disna gie a guaranteed phone-trainin schedule. A realistic goal is mair accurate, comfortable entry in the conditions ye uise, judged wi comparable material insteid o a promised percentile or universal WPM target. Keith et al., 2007 Feit et al., 2016
Gie the related free test a try
Ty-pin speed test
Try the ty-pin test in touchscreen mode, then compare later phone runs wi that mode. Results fae a physical keyboard belang in a separate comparison. The published values gie context for the task; they are nae Helibrain player percentiles.
Common questions
Is 36 WPM normal for ilka phone user?
It is the approximate mean for this volunteer sample, no a representative norm for ilka age, kintra or keyboard. Palin et al., 2019
Are women or men faster on phones?
The hale-sample means war nearly the same, while patterns in subgroups differed. Thae data dinna justify a general rule aboot an individual. Palin et al., 2019
Soud I turn prediction aff?
The observational link disna settil the best settin for you. Compare equivalent tasks an correctness afore ye put a difference doun tae a feature. Palin et al., 2019 Soukoreff et al., 2003
Sources
- Dhakal et al. (2018). Observations on Ty-pin fae 136 Million Keystrokes.
- Palin et al. (2019). Hoo dae fowk ty-pe on mobile devices?.
- Feit et al. (2016). Hoo we Ty-pe.
- Soukoreff et al. (2003). Measures for text-entry research.
- Soukoreff et al. (2001). Measurin errors in text-entry tasks.
- MacKenzie and Soukoreff (2002). Text Entry for Mobile Computing: Models and Methods, Theory and Practice.
- Salthouse (1986). Perceptual, cognitive, and motoric aspects o transcription ty-pin..
- Keith et al. (2007). A deliberate-practice account o ty-pin skill in ilkday typists..
Cite this page: Whit’s the average ty-pin speed on a smartphone?. Helibrain. 2026. https://helibrain.com/sco/answers/average-smartphone-typing-speed. CC BY 4.0.
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