Rarest MBTI Types for Women: A UK Sample, Not a Global Ranking

The Myers-Briggs Company's UK European-English supplement to the MBTI Manual for the Global Step I and Step II assessments reports this distribution in Table 7. The table covers 1,487 women within a total UK sample of 2,831. Its counts are reported Global Step I types, not an estimate combining different internet tests.

The report collected best-fit type information, but says those results are not reported here. This table describes reported assessment results, not independently confirmed personal identities, and does not combine labels from different models.

Read the original UK supplement, Table 7, printed page 7 to check the numbers. The document's copyright is 2018; an exact fieldwork period is not provided in the inspected methodology. These are not newly collected 2026 prevalence figures.

Every row below uses the same denominator: 1,487 UK English-reading women. Counts and percentages are transcribed as reported in Table 7; percentages are rounded to one decimal place. The order follows the source's type table, rather than creating an apparent worldwide ranking.

The counts total 1,487, and the published percentages total 100.0%. ENTJ has the lowest count in this sample, but ENTJ and INTJ share the lowest displayed percentage, 1.5%. The one-person count difference does not establish a meaningful population difference. No confidence intervals or claim of statistical separation between these two types is supplied here.

Source: UK supplement, Table 7, printed page 7 / physical PDF page 8. All rows belong to this one sample. The data do not establish the rarest type among American, Korean or worldwide women.

The report describes a UK sample of people who read European English, obtained through a market-research firm. Sampling targets were informed by the UK population and designed around the working-age population. The methodology describes online administration of the research form and market-research panel participation, with incentives for most samples in the broader project. The report calls the resulting UK sample representative; that is the report's characterization, not evidence that it represents women in every country or every age group today.

The full sample included people aged 16–67, with an average age of 38 and a standard deviation of 12.9 years. All reported residing in the UK. These age statistics describe the full 2,831-person sample; a separate age distribution for the 1,487 women is not given in the inspected passage.

This page cannot evaluate a current population ranking from the table alone. Exact collection dates, gender-specific age detail and selection-bias uncertainty are not supplied here. Changing the people recruited, language, assessment or time period can change the observed distribution. Do not average this female table with another dataset and call the result a global estimate.

No. In this particular table, INFJ has 51 participants and a reported 3.4%, while INTJ has 23 and 1.5%. ENTJ has 22 and the same displayed 1.5%. The source therefore does not support saying that INFJ is the rarest female type in this sample, or that one type is universally rarest among women.

A claim from another study needs its own female denominator, recruitment method, assessment, region and dates. A total-population distribution is not interchangeable with a women-only distribution. Our overall MBTI percentage explainer discusses a separate US English sample; it is not a second female sample corroborating this table.

A small count in one sample says how often a label appeared among those participants. It does not show that someone is more intelligent, a better leader, less feminine, more creative or better suited to a particular career. It also does not explain why a distribution differs by gender.

Within the MBTI framework, type describes preferences, not a ranking of competence or personal value. The Myers & Briggs Foundation's preference explanation provides the model context; it is not another prevalence dataset.

If you are considering your own type, focus on specific preferences and examples from your life rather than trying to obtain an uncommon result. For example, reflect on how you gather information and weigh decisions, then compare that reflection with the model's explanations. A rare label does not prescribe how you should behave.