Since you have a clear explanation for the outlier (i.e., entry error), you could correct it if you do not want to delete it. The following chapter can be helpful.

Beaumont, J.-F., & Rivest, L.-P. (2009). Chapter 11—Dealing with outliers in survey data. In C. R. Rao (Ed.), Handbook of statistics (Vol. 29, pp. 247–279). Elsevier. https://doi.org/10.1016/S0169-7161(08)00011-4

Good luck,

It is a trivial and unique error, according to what you write.

Therefore, you can leave it out.

Moreover, it is clear that this was a typo due to the "hurry to answer", typical of young people.

This is most likely a "younger" 20-year-old (so, you already have a clear indication of the value of the field).

Incidentally: how much is the reference population and how did you size the sample?

You wrote: "I have already done a selection on the sample to only keep participants who have completed all data (exclusion of any missing data)." This practice (listwise deletion) is not necessary, and it is also not recommended. It is better to retain all participants that provide some valid scores and to address missing values either by multiple imputation or full information maximum likelihood (FIML) to make use of all available data points. This applies also to age (I don't see why you couldn't, for example, impute missing values for age.).

The main arguments against listwise deletion are that you loose (a lot of!) data resulting in a loss of statistical power, and that listwise deletion can lead to bias when the data are not missing completely at random (MCAR). (Multiple imputation and FIML only require the less restrictive assumption of missing at random [MAR] data.)

Enders, C. K. (2022). Applied missing data analysis (2nd ed.). Guilford Press.

Schafer, J. L., & Graham, J. W. (2002). Missing data: Our view of the state of the art. Psychological Methods, 7(2), 147–177. https://doi.org/10.1037/1082-989X.7.2.147

substitute the value by the average of age for all participants.