Key evidence & numbers
| Group in the mobile study | Mean WPM | Context |
|---|---|---|
| Full sample | 36.17 | SD 13.22; English transcription |
| Ages 10–19 | 39.6 | Age-group comparison, not longitudinal change |
| 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 reported in the results and Table 3; the age values are in Table 4. The sample's mean age was 24.1 years and recruitment was online. Younger volunteers and people comfortable with English typing were prominent, so the table should not be used to label every person of a given age slow or fast. Palin et al., 2019
Is a phone always slower than a keyboard?
The authors compared their mobile results with a large desktop study using a related method. The desktop study's mean was 51.56 WPM. That is useful context, but it is not a randomised comparison of every participant on both devices. Subtracting the means does not predict how much faster you personally would type after switching. Palin et al., 2019 Dhakal et al., 2018
Everyday techniques also differ. Motion-capture research shows that self-taught physical-keyboard users can perform well without following one prescribed finger pattern. Mobile entry adds touchscreen targeting and assistance features, so the desktop ten-finger rule is not an appropriate phone benchmark. Feit et al., 2016 MacKenzie and Soukoreff, 2002
What does the error percentage mean?
The 2.34% value concerns errors remaining in the transcribed output under the study's metric. It is not the fraction of all touches that were wrong, because some errors were corrected. Text-entry methodology distinguishes final errors from correction effort, and neither should be hidden by reporting speed alone. Soukoreff et al., 2003 Soukoreff et al., 2001
What affects it?
Age, sex and language effects need context
The full mobile sample's female and male means were 36.0 and 36.1 WPM, respectively, despite a statistical difference in the large dataset. The practical difference in those full-sample means is tiny. The U.S. subsample showed a different pattern, illustrating why a result's significance and its generalisability are separate questions. Palin et al., 2019
Native English users averaged 37.8 WPM in the same table. That association is not proof that changing a phone's language setting causes a specific improvement. The task required English transcription, and differences in familiarity can affect reading and entering the supplied text. Palin et al., 2019 Salthouse, 1986
Do autocorrect and word prediction help?
In this dataset, autocorrection use was positively associated with speed, whereas selecting predicted words was negatively associated. The study was observational, so those associations do not prove that enabling one feature will cause the same gain for every user. People choose features for different reasons, including their existing skill and error patterns. Palin et al., 2019
For a useful personal comparison, keep keyboard layout, assistance settings, language and task type consistent. Record correctness as well as WPM. A messaging conversation, a memorised sentence and an unfamiliar transcription passage measure different combinations of language production and motor entry. MacKenzie and Soukoreff, 2002 Soukoreff et al., 2003
Practice can improve task-specific proficiency, but everyday-typing research does not provide a guaranteed phone-training schedule. A realistic goal is more accurate, comfortable entry under the conditions you use, evaluated with comparable material rather than a promised percentile or universal WPM target. Keith et al., 2007 Feit et al., 2016
Try the related free test
Typing speed test
Try the typing test using the touchscreen mode and compare later phone runs with that mode. Physical-keyboard results belong in a separate comparison. The published values provide task context; they are not Helibrain player percentiles.
Common questions
Is 36 WPM normal for every phone user?
It is the approximate mean of this volunteer sample, not a representative norm for every age, country or keyboard. Palin et al., 2019
Are women or men faster on phones?
The full-sample means were almost identical, while subgroup patterns differed. Those data do not justify a broad rule about an individual. Palin et al., 2019
Should I turn off prediction?
The observational association does not establish the best setting for you. Compare equivalent tasks and correctness before attributing a difference to a feature. Palin et al., 2019 Soukoreff et al., 2003
Sources
- Dhakal et al. (2018). Observations on Typing from 136 Million Keystrokes.
- Palin et al. (2019). How do People Type on Mobile Devices?.
- Feit et al. (2016). How We Type.
- Soukoreff et al. (2003). Metrics for text entry research.
- Soukoreff et al. (2001). Measuring 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 of transcription typing..
- Keith et al. (2007). A deliberate practice account of typing proficiency in everyday typists..
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