To translate a t-test, ANOVA, chi-square test or degrees of freedom accurately, you have to preserve the statistical question, not just the statistic printed in the source. A sentence such as “Welch’s t-test found a difference between group means, t(18.7)=2.41, p=.027” contains several linked ideas: which test was used, what quantity was compared, which version of the test was chosen, how the test statistic is reported, what the degrees of freedom mean, and what conclusion the author is entitled to draw. A fluent translation can still be statistically wrong if any one of those relationships changes.
This guide explains how to translate hypothesis tests, t-tests, analysis of variance (ANOVA), chi-square tests, test statistics, one-tailed and two-tailed tests, and degrees of freedom for research papers, technical reports, educational materials, data dashboards and exam questions. It is written for translators, students, editors and subject specialists who need to move statistical meaning between languages without turning “no evidence of a difference” into “the groups are the same,” a two-sample test into a paired test, or a chi-square test of independence into a generic test of significance.
The central method is simple: identify the claim structure before translating the prose. Record the null hypothesis, alternative hypothesis, groups or variables involved, test family, test statistic, degrees of freedom, significance level or p-value, direction of the alternative, assumptions and stated conclusion. Then rebuild the sentence naturally in the target language while keeping those relationships fixed. This article complements eduKateSG’s existing guides to p-values and statistical significance, confidence intervals and standard errors, and standard deviation and variance. Its narrower job is to preserve the logic of the test itself.
A sixty-second orientation: a statistical test is a structured argument
A classical hypothesis test begins with a null hypothesis and an alternative hypothesis. A test statistic is calculated from the data. Under the null hypothesis, that statistic has a reference distribution whose shape depends on the test and often on degrees of freedom. The observed statistic is then compared with what would be expected under the null. The source may express the decision through a critical value, a p-value, or both. NIST’s statistical handbook describes this general logic and shows, for example, how Student’s t and chi-square distributions use degrees of freedom when defining critical values and rejection regions.
Translation becomes dangerous when this structure is compressed into a few familiar words. “Significant” can be translated as if it meant important. “Reject the null” can be turned into “prove the alternative.” “No significant difference” can become “no difference.” “Two-tailed” can be lost because a target sentence sounds smoother without it. The translator’s task is to preserve the test as an argument with conditions, not to rewrite the result as a stronger everyday claim.
1. Translate the hypothesis before translating the result
Suppose a fictional study compares the mean reading time of two groups. The null hypothesis is that the population means are equal. The alternative is that they differ. Before you translate a sentence reporting the test, write those hypotheses in plain language: “The test asks whether the observed difference is compatible with equal population means under the stated model.” This sentence acts as a control. If the target wording begins to suggest that the sample means must be identical under the null, or that a small p-value proves a specific causal mechanism, you can see that the translation has moved beyond the source.
The null hypothesis is not automatically the author’s preferred belief, and the alternative is not automatically the claim the author “wants to prove.” Those motivational stories may be absent from the research design. Translate the hypotheses as statistical propositions. If the source uses symbols such as H0 and H1 or Ha, preserve them according to the document’s notation rather than normalising every paper to one house preference.
A strong translation also keeps the population parameter separate from the sample statistic. A hypothesis about μ concerns a population mean, not the observed sample mean x̄. A hypothesis about p may concern a population proportion, not the p-value. The same letter can have different meanings in statistics, so translate the surrounding noun phrase, not the isolated symbol.
2. “Fail to reject” is not the same as “accept”
One of the most common translation errors occurs when “fail to reject the null hypothesis” is shortened to “accept the null hypothesis.” In ordinary conversation, those statements can sound equivalent. In hypothesis testing, they are not. A study may fail to produce sufficient evidence against H0 because the null is approximately correct, because the sample is small, because the data are noisy, because the effect is smaller than the study can detect, or because assumptions and measurement choices limit sensitivity.
Preserve the evidential direction. “The test did not find evidence of a difference” is usually safer than “the test showed that there was no difference,” unless the design and analysis explicitly support an equivalence or non-inferiority conclusion. Equivalence testing is a different inferential framework with predefined margins; it should not be invented by translating a non-significant result more confidently.
This distinction is especially important in abstracts and executive summaries where statistical language is compressed. If the source carefully says “did not reach statistical significance,” the target should not silently upgrade that to “was ineffective” or “had no effect.” A translator preserves the level of evidence the authors reported, even when a stronger sentence would be easier to read.
3. Independent-samples and paired t-tests ask different questions
An independent-samples t-test compares means from observations treated as independent across two groups. A paired t-test works with linked observations, such as before-and-after measurements on the same participant or deliberately matched pairs. The mathematics and degrees of freedom differ because the data structure differs. Translating both as simply “a t-test between two groups” can erase the design.
Consider a fictional class measured before and after a training exercise. The relevant quantity in a paired analysis is the within-person difference. If the source says “a paired t-test showed that mean completion time decreased,” the target should keep paired or an equivalent description. Writing “the experimental group was compared with the control group” would invent a second independent group that does not exist.
The reverse error is also possible. Two separate cohorts measured once each should not become “pre-test” and “post-test” observations merely because one was studied earlier. Time order does not create pairing. Pairing requires a defined link between observations. In a translation workflow, record the unit of analysis and matching rule before choosing a target term for the test.
4. Student’s t-test and Welch’s t-test should not be collapsed
Two-sample t-tests appear in different forms. A pooled-variance version assumes equal population variances under its model. Welch’s t-test does not require that equal-variance assumption and generally uses a degrees-of-freedom calculation that can produce a non-integer value. If the source identifies Welch’s test, retain that identifier. It tells the reader something about the statistical method, not merely about the software used.
Suppose a report gives t(18.7)=2.41. A translator unfamiliar with Welch’s test may “correct” 18.7 to 19 because degrees of freedom sound like a count and counts are usually whole numbers. That edit would be wrong. Welch–Satterthwaite degrees of freedom can be fractional because they are an approximation derived from sample variances and sizes.
Do not explain away fractional degrees of freedom unless the source or editorial brief calls for teaching material. In a research translation, simply preserve the method and value. In an educational adaptation, you may add that fractional degrees of freedom are expected for Welch’s test. The important point is that apparent typographic oddity is sometimes a genuine feature of the analysis.
5. Degrees of freedom are not just “sample size minus one”
The phrase degrees of freedom is often abbreviated df. In some simple settings, df equals n−1. For a one-sample t-test, that relationship is common. But degrees of freedom depend on the model and test. A two-sample pooled t-test has one formula, Welch’s test has another, one-factor ANOVA separates numerator and denominator degrees of freedom, and contingency-table chi-square tests use dimensions of the table.
Therefore, never translate df as if it were a synonym for “number of participants.” It is related to the amount of independent information available after estimating parameters or imposing constraints. That general idea is safer than a universal formula. If the source writes F(2,57)=4.31, both degrees-of-freedom values belong to the F statistic and should remain in the same order.
A useful review check is to compare the reported df with the described design. Three groups in a simple one-way ANOVA often produce a numerator df of two. If the source instead reports F(3,56), there may be four groups, another factor, or a different model. Do not “fix” it from intuition. Use the discrepancy to ask a source question when necessary.
6. One-tailed and two-tailed tests preserve direction
A two-tailed alternative allows departure in either direction. A one-tailed alternative specifies a direction before examining the result under the stated design. NIST’s Student’s t critical-value guidance distinguishes one-sided and two-sided rejection regions. Translation should preserve that distinction because the same observed statistic can lead to different p-values or critical thresholds depending on the alternative.
Do not convert “greater than” into “different from” or vice versa merely because the target language prefers a shorter sentence. Ha: μ>μ0 is directional. Ha: μ≠μ0 is two-sided. These statements ask different questions. If the source says “one-sided test” but the written hypothesis appears two-sided, preserve both pieces of evidence in a query rather than deciding which is correct.
Words such as increase, decrease, higher, lower, exceeds and differs are therefore statistical operators as well as ordinary vocabulary. During revision, check them separately from style. A polished target sentence that reverses higher and lower can invalidate the entire test report while leaving every numeral unchanged.
7. ANOVA asks about a set of means, not automatically every pair
One-factor analysis of variance generalises the two-group mean comparison to multiple levels of one factor. NIST describes one-factor ANOVA as a test of equal means across groups. If the omnibus ANOVA is significant, the conclusion is that the data provide evidence that not all relevant population means are equal under the model. It does not by itself identify which specific groups differ.
This is a major translation trap. A source might say “ANOVA indicated a group effect, F(3,76)=5.12, p=.003.” A target sentence saying “all four groups differed significantly from one another” adds pairwise conclusions that the omnibus test does not establish. Post-hoc comparisons, planned contrasts or other follow-up analyses are needed to support those more specific statements.
Preserve the level of the claim. “There was evidence of a difference among group means” is not the same as “each group differed from every other group.” In educational writing, explain the distinction explicitly. In research translation, keep the author’s exact scope and verify that follow-up-test language remains attached to the correct comparisons.
8. The word “variance” in ANOVA does not mean the test is only about variances
The name analysis of variance can confuse readers because one-way ANOVA is commonly used to test differences among means. It does this by comparing sources of variability through an F statistic. A translator who reads the name literally may write that ANOVA “tests whether the group variances are equal.” That is not the primary question of the standard one-way mean-comparison ANOVA.
There are separate procedures for examining equality of variances, such as Levene’s test or Bartlett’s test under their respective assumptions. Keep those tests distinct. If a methods section says “homogeneity of variance was assessed before ANOVA,” the preliminary variance check and the ANOVA are two different analytical steps.
A useful target phrase is “analysis of variance (ANOVA), used here to compare group means,” when the audience needs orientation and the source permits explanatory wording. This small clarification prevents a literal-language misunderstanding without changing the statistical method. Translation sometimes improves readability by making the function of a technical label explicit, but only when the explanation is accurate.
9. Translate the F statistic with both degrees of freedom
An ANOVA result may appear as F(2,57)=6.40, p=.003. The two df values play different roles in the F distribution. The first is commonly associated with the numerator or model component; the second with the denominator or error component in a simple one-way design. Preserve the order exactly.
A target editor may be tempted to write F(57,2) because larger sample-related numbers seem as though they should come first. Do not reorder statistical notation according to ordinary prose logic. Likewise, do not translate F into a target-language initial. The letter is a conventional symbol for the statistic, not an English abbreviation requiring localisation.
If a source gives only F=6.40 without df, translate what is present. Do not add guessed degrees of freedom from the group count unless the editorial task explicitly includes reconstructing missing statistical information. Missing reporting detail is not permission to manufacture data.
10. Repeated-measures ANOVA is not ordinary one-way ANOVA
When the same participants are measured repeatedly, observations are dependent across conditions or times. Repeated-measures ANOVA models that structure differently from an ordinary independent-groups ANOVA. A translation that removes “repeated measures” can make the design appear to involve separate groups.
The words within-subjects, between-subjects, repeated measures, condition, time point and interaction often carry the design logic. Translate them consistently. In a mixed design, a between-subject factor and a within-subject factor coexist. The reader needs to know which effect belongs to which structure.
Corrections such as Greenhouse–Geisser or Huynh–Feldt may modify degrees of freedom when sphericity-related assumptions are addressed. This can produce fractional df in repeated-measures ANOVA. As with Welch’s test, do not “clean” these values into integers. Statistical notation that looks unusual may be evidence that the analysis accounted for an assumption.
11. Chi-square is a family of tests, not one universal procedure
The label chi-square appears in several contexts. A chi-square goodness-of-fit test compares observed category counts with counts expected under a specified distribution. A chi-square test of independence examines association between categorical variables in a contingency table. A chi-square test can also be used for a population variance under particular assumptions. NIST documents these as distinct procedures.
Therefore, “chi-square test” is sometimes too broad for a target audience. If the source specifies goodness of fit, independence, homogeneity or variance, preserve the qualifier. It tells the reader what null hypothesis is being tested. Do not translate every χ² result as a “test of association” merely because contingency-table examples are common in textbooks.
The symbol χ² should usually remain a symbol. If your target publication requires words, use the established target-language name for the chi-square distribution or test, but keep the formula and reporting notation consistent. Do not confuse χ with the letter X; font and encoding problems can create visually similar but semantically different characters.
12. Chi-square tests work with counts, not percentages alone
Contingency-table chi-square calculations are based on observed and expected counts. A report may display percentages for readability, but those percentages arise from counts and denominators. Translating only the percentages while losing the row or column totals can make the result impossible to interpret.
Suppose a fictional table shows 60% of group A and 45% of group B selecting an option. Without group sizes, those percentages do not tell you the observed counts. A 15-percentage-point difference based on 10 people per group carries different statistical information from the same percentage difference based on 1,000 people per group.
In translation, preserve n values, total counts and percentage labels. If a table heading says “row %,” do not turn it into “percentage of total” because the denominator changes. Statistical conclusions depend on those denominators even when the table appears visually similar after localisation.
13. Expected counts are model quantities, not predictions in the ordinary sense
In a chi-square test of independence, expected counts are calculated under the null model of independence using marginal totals. The word expected here is technical. It does not mean the researcher subjectively predicted those exact counts before seeing the data. Translating expected as forecast, planned or anticipated can misrepresent the method.
A safer educational explanation is “the counts expected if the variables were independent under the null model.” This keeps the conditional nature of the quantity visible. Observed count means the count actually recorded in the table. The chi-square statistic summarises discrepancies between observed and expected counts in relation to expected magnitudes.
Assumption checks involving small expected counts should also remain attached to expected, not observed, counts. If the source says some expected cells were below a threshold, do not rewrite that as “some categories had fewer than five observations” unless that statement is separately true.
14. Degrees of freedom in a contingency table come from table structure
For a standard r-by-c chi-square test of independence, the degrees of freedom are commonly (r−1)(c−1). A 2×3 table therefore has two degrees of freedom. This is a useful verification rule when translating a result such as χ²(2)=7.81.
But verification is not authorisation to overwrite the source. If a 2×3 table is reported with χ²(1), the table may have been collapsed for analysis, a different contrast may have been tested, a correction or alternative model may be involved, or the source may contain an error. Record the mismatch and investigate rather than silently replacing the df.
The broader principle is transferable: use statistical relationships to detect possible translation or source problems, then preserve an audit trail. Translation quality improves when the translator can recognise impossible-looking combinations without assuming that every unfamiliar result must be wrong.
15. Test statistic names should remain attached to their tests
A t statistic belongs to a t-test framework; an F statistic belongs to an F distribution and appears in ANOVA and other models; χ² belongs to chi-square procedures. These symbols are not interchangeable generic measures of “how significant” a result is. A larger value can mean different things depending on degrees of freedom, direction and the relevant reference distribution.
A translation should therefore avoid sentences such as “the significance score was 4.2” when the source says t=4.2. Call it a test statistic, t statistic, F statistic or chi-square statistic according to the source. The term score may be natural in everyday target-language writing but can imply an entirely different quantity.
Keep symbols and names in agreement. If the prose says ANOVA but the statistic is t, verify whether the source reports a planned contrast, a separate t-test or an inconsistency. Statistical typography can serve as a cross-check on terminology when both are present.
16. P-values belong to the test, not to the effect itself
This guide does not repeat the full treatment of p-values, but one translation rule is essential: the p-value describes the compatibility of the observed test statistic and more extreme results with the null model under the specified test. It is not the probability that the effect is real, the probability the null is true, or the size of the effect.
When a t-test result gives t(38)=2.30, p=.027, keep the p-value attached to that test result. If a later sentence reports an effect size such as Cohen’s d, do not merge the two into one “significance magnitude.” Statistical significance and effect magnitude answer different questions.
Likewise, “p<.05” should not become “95% confidence that the alternative is correct.” Preserve the inequality and, if an explanatory translation is permitted, use a statistically defensible description. For the complete p-value treatment, link or refer to the dedicated eduKateSG article rather than stretching this article into a second broad owner.
17. Translate assumptions as conditions, not guarantees
Statistical tests are derived under assumptions about data structure and model behaviour. Common examples include independence, distributional assumptions, variance conditions or sufficient expected counts. A methods section may state that assumptions were checked, considered reasonable, violated, corrected for or addressed through an alternative procedure.
Preserve the strength of that language. “No evidence of serious violation was found” is weaker than “the assumptions were proven true.” “Approximately normal” is not “perfectly normal.” “Robust to moderate departures” is not “assumption-free.” Translation should not turn statistical caution into certainty.
Also keep the assumption linked to the correct object. In a paired t-test, normality often concerns the distribution of paired differences, not necessarily each raw measurement separately. In ANOVA, terminology may concern residuals rather than the outcome variable in isolation. When the source is precise, the target should remain precise.
18. “Parametric” does not mean “more accurate”
Parametric tests use models containing parameters and assumptions about distributions or their derived sampling behaviour. Nonparametric or rank-based procedures use different structures. Translating parametric as “precise,” “advanced” or “strong” can imply a hierarchy that the source does not state.
A Mann–Whitney test is not simply a “nonparametric t-test” in every interpretive sense, even though it is sometimes introduced as an alternative for two independent groups. What it tests and how it should be interpreted depend on assumptions and context. Keep the actual test name when the source provides it.
The safest translation method is conceptual rather than prestige-based: identify what quantity or distributional relationship the procedure evaluates, preserve the named test, and avoid adding adjectives that suggest one method is inherently superior. Statistical method choice is an analytic decision, not a linguistic ranking.
19. Post-hoc tests and planned contrasts are not decorative follow-ups
After an omnibus ANOVA, researchers may conduct post-hoc comparisons or planned contrasts. These analyses answer more specific questions than the omnibus test. Their names—Tukey, Dunnett, Bonferroni-adjusted comparisons, planned contrast and others—carry methodological information. Preserve them when the source includes them.
A translation error can occur when “post-hoc” is interpreted as “afterwards” in a purely chronological sense. In statistics, the phrase often identifies a particular role for comparisons conducted after or in response to an omnibus analysis, frequently with procedures that address multiple comparison error. Translate the concept, not merely the Latin surface form.
Likewise, planned contrasts may have been specified before examining the data and may not be interchangeable with exploratory pairwise comparisons. If the source distinguishes them, the target should not collapse them under a generic “additional tests” heading.
20. Multiple testing changes the evidence context
When many hypotheses are tested, the chance of obtaining at least some small p-values under null conditions can increase. A study may control family-wise error, false discovery rate or another quantity using a stated procedure. The translator should preserve the adjustment method and the set of tests to which it applies.
Do not translate “adjusted p-value” as “corrected result” in a way that suggests the original measurement was wrong. The adjustment concerns the inferential procedure across multiple tests. Similarly, “Bonferroni correction” is a named method; it does not mean that every p-value was arithmetically repaired after an error.
A good review asks whether words such as adjusted, unadjusted, corrected, family, discovery and comparison remain attached to the right quantities. This vocabulary may look peripheral, but it can determine the evidential threshold used by the analysis.
21. Software output is not self-explanatory
Statistical software may print compact labels such as df, Sig., Pr(>F), t value, Pearson Chi-Square, Asymp. Sig., Mean Square or Sum Sq. A translation project may require localising interface labels, explaining output in prose, or preserving the output exactly as evidence. These are different tasks.
Do not translate a protected software column if the screenshot or reproducible output needs to remain identical to the original application. Instead, explain the label in surrounding text or a legend. If the output is being reconstructed in the target language, confirm which terms are application strings and which are author-created headings.
A label such as “Sig.” may represent a p-value field in a particular package, but that does not make significance a synonym for probability. Translate the functional meaning appropriate to the document while preserving the software’s data values and method names.
22. A complete worked t-test translation
Consider this fictional source: “Participants using method A completed the task in a mean of 42.1 seconds (SD 6.3), while participants using method B required 46.8 seconds (SD 7.1). Welch’s independent-samples t-test indicated a difference in mean completion time, t(37.6)=−2.18, p=.035. The confidence interval for the mean difference excluded zero.”
Before translating, record the structure. There are two independent groups. The outcome is completion time in seconds. Lower values mean faster completion. Welch’s t-test was used. The statistic is negative because the difference was defined in a direction consistent with A minus B or an equivalent coding. Degrees of freedom are fractional. The p-value is .035. The source states evidence of a difference, not proof that the method caused the difference unless the design supports causal inference.
A flawed translation might say: “Method A was significantly 2.18 seconds faster and the probability that both methods are equal was 3.5%.” This confuses the t statistic with a time difference and the p-value with a posterior probability. The actual sample mean difference is 4.7 seconds in magnitude. The t statistic is unitless. A repaired translation keeps each quantity in its proper role.
Notice also that the sign of t should not be casually removed. It may encode the direction created by the software’s group order. The prose can state the direction in meaningful terms, but the reported statistic should remain as printed unless the journal’s style requires another convention and the analysis is correspondingly re-expressed.
23. A complete worked ANOVA translation
Use another fictional source: “Mean scores differed across four instruction conditions, F(3,116)=4.92, p=.003. Tukey-adjusted comparisons indicated that condition D exceeded conditions A and B, while the remaining pairwise comparisons were not statistically significant at the stated family-wise level.”
The omnibus result supports the statement that not all four population means are equal under the model. The Tukey follow-up provides the specific pairwise pattern. A target that says “ANOVA proved D was better than every other condition” is wrong twice: ANOVA alone does not identify the pair, and the source does not say D differed from C.
Keep “Tukey-adjusted” attached to the comparisons. The adjustment belongs to the inferential procedure, not to the raw scores. Keep “family-wise” with the error criterion when translated. If the target audience is general, you may explain that the post-hoc procedure controls the overall false-positive risk across the specified comparison family, but do not invent a numerical family-wise rate unless the source gives it.
The result also illustrates why “not statistically significant” should not become “equal.” Some pairs may have uncertain differences that the study could not distinguish from zero at the chosen threshold. Preserve the evidence claim rather than replacing it with a stronger equality claim.
24. A complete worked chi-square translation
Consider a fictional 3×2 table relating study strategy category to whether learners completed a course. The source reports: “Completion status was associated with strategy category, Pearson χ²(2)=9.64, p=.008. Standardised residuals suggested that the largest departure from independence occurred in the structured-practice category.”
The test of independence asks whether the categorical variables are associated under the model. It does not establish that the strategy caused completion. It does not mean every cell differed meaningfully from expectation. The residual sentence identifies where the largest discrepancy occurred.
A flawed translation might say: “Pearson correlation was 9.64 and proved structured practice caused completion.” This confuses Pearson’s chi-square statistic with Pearson correlation and upgrades association to causation. The proper target retains the χ² symbol or established test name, the df, the p-value and the observational scope.
Also keep the word standardised attached to residuals if that is what the source reports. Raw residuals, Pearson residuals and standardised residuals are not automatically identical quantities. Technical adjectives can determine how a diagnostic number should be interpreted.
25. Translate tables by preserving hierarchy
Statistical tables often compress an entire argument into headings. A typical table may include Test, Statistic, df, p, Effect Size and Confidence Interval. Translate the hierarchy first. Determine whether a subheading belongs to one test or the whole table. Keep units in the heading when all rows share them.
Avoid turning a dash into zero. A dash may mean not reported, not applicable, not estimated or intentionally omitted. The meaning should be defined by the source or table note. This issue connects directly to the separate eduKateSG translation work on missing data and placeholders: absence is not a numerical value.
Footnotes such as “Welch correction applied,” “two-sided p-values,” or “expected count below five in one cell” are part of the statistical meaning. Do not remove them as layout clutter. A table that looks cleaner but loses its assumptions and reporting notes is not a faithful translation.
26. Practice clinic: repair the translation, not the statistics
Practice one. Source: “A paired t-test compared pre-test and post-test scores.” Faulty target: “An independent t-test compared two groups.” Repair: preserve pairing and the repeated observations. The source describes two measurements on linked units, not two independent groups.
Practice two. Source: “t(24)=1.10, p=.28; the difference was not statistically significant.” Faulty target: “The groups were proven equal.” Repair: say that the test did not provide sufficient evidence of a difference at the stated criterion. Equality requires different support.
Practice three. Source: “Welch’s t-test, t(17.4)=2.7.” Faulty target: “t(17)=2.7.” Repair: keep 17.4. Fractional df are expected in Welch-type calculations.
Practice four. Source: “ANOVA found an overall condition effect.” Faulty target: “Every condition differed from every other condition.” Repair: retain the omnibus scope unless follow-up comparisons establish the pairwise pattern.
Practice five. Source: “χ²(2)=6.2.” Faulty target: “Pearson correlation r=6.2.” Repair: χ² is a chi-square statistic, not a correlation coefficient. Preserve the test family and symbol.
Practice six. Source: “One-sided alternative: mean>50.” Faulty target: “The mean differs from 50.” Repair: preserve the directional greater-than alternative.
Practice seven. Source: “p=.049 after multiplicity adjustment.” Faulty target: “The unadjusted chance of error was 4.9%.” Repair: preserve the adjusted p-value and avoid redefining it as an error probability.
Practice eight. Source: “Expected cell count was 3.8.” Faulty target: “Only 3.8 participants were observed.” Repair: expected count is a model-derived quantity and need not be an integer; observed people are counted in whole units.
27. Frequently asked translation questions
Should I translate the names t, F and chi-square? Translate the surrounding technical term according to established target-language usage, but preserve conventional symbols such as t, F and χ² unless the publication standard explicitly requires another notation.
Can degrees of freedom be decimal? Yes. Welch-type tests and corrected repeated-measures analyses can report fractional degrees of freedom. Do not round them merely because df often look like counts.
Does a significant ANOVA mean every group differs? No. The omnibus result indicates that the equal-means model is inconsistent with the data at the chosen criterion; follow-up analyses are needed for specific pairwise claims.
Does a non-significant t-test prove two means are equal? No. Failure to reject a difference test is not equivalent to demonstrating equivalence. Preserve the source’s actual evidential claim.
Are all chi-square tests tests of independence? No. Chi-square procedures include goodness-of-fit, independence and variance-related tests among others. Keep the source qualifier.
What is the best release check? Write the test in one sentence: “This test compares ___ under the null hypothesis ___ using statistic ___ with df ___ and concludes ___.” If the target version fills those blanks differently from the source, review the translation.
28. Connect this specialist guide to the broader eduKateSG translation system
This article is a specialist branch of Master Art of Translation, the conceptual owner for the eduKateSG translation architecture. It should be read alongside Translate | P-Values, Statistical Significance and Effect Sizes, Translate | Sample Size, Statistical Power, Type I and Type II Errors, and Translate | Randomised Controlled Trials. For word meaning and terminology precision, use the protected Vocabulary Learning Hub; for grammatical relationships such as comparison, negation and scope, use How English Works.
The final discipline is simple: never let smooth prose erase the test design. Preserve which observations are linked, which hypothesis is directional, which statistic belongs to which procedure, which degrees of freedom were reported, which assumptions were checked, and how strong the conclusion actually is. A high-quality statistical translation does not merely copy mathematical symbols correctly. It keeps the inferential argument intact while making that argument readable in another language.