HCS 202 Stats by Chat

Single-sample t test is used to compare _

one sample mean to a known population mean

Dependent/Paired-samples t test is used when _

same participants are measured twice or matched pairs

Independent-samples t test is used to compare _

the means of two unrelated groups

Degrees of freedom for single sample and dependent t tests = _

N − 1

Degrees of freedom for independent samples t test = _

N₁ + N₂ − 2

In Jamovi, single sample t tests compare _

sample mean to a population mean

In Jamovi, dependent t tests compare _

two related conditions within subjects

In Jamovi, independent t tests compare _

two separate groups

Cohen’s d formula for single/dependent t tests = _

mean difference ÷ SD of differences

Cohen’s d formula for independent t tests = _

(M₁ − M₂) ÷ sₚₒₒₗₑd
M → Mean of each group

S_pooled → Average of two groups variance

Cohen’s d ≈ 0.20 means _

small effect

Cohen’s d ≈ 0.50 means _

medium effect

Cohen’s d ≥ 0.80 means _

large effect

APA format for reporting t = _

t(df) = value, p < .05 (all italic and lowercase)

In APA results, always answer _

Was null rejected, how big was effect, what type of error possible

A large |t| value means _

greater difference relative to error → likely reject H₀

Assumption for all t tests includes _

independence of observations

Single-sample t test assumption

normality of the sample distribution;

Pass Condition = Shapiro p > .05

Dependent t test assumption

normality of the difference scores;

Pass Condition = Shapiro p > .05

Independent t test assumptions

Normality and homogeneity of variance (Levene’s test);

Pass Condition = Shapiro p > .05 and Levene p > .05

Levene’s test p > .05 means _

homogeneity assumption met

Formula for t = _

(observed difference) ÷ standard error

If p < .05 then _

reject the null hypothesis

If p > .05 then _

fail to reject the null hypothesis

Confidence interval bounds are found by _

LB = M − MOE, UB = M + MOE
M → Sample Mean; MOE → Margin of Error (t * SE)

A wide CI means _

less precision in the estimate

CI includes zero → _

difference is not statistically significant

One-tailed test is used when _

prediction is in a specific direction

Two-tailed test is used when _

any difference is being tested

Critical region for one-tailed test _

in one tail of the distribution

Critical region for two-tailed test _

split across both tails

In Jamovi output, check for _

t, p, df, mean difference, CI, effect size

For independent samples, Jamovi also shows _

Levene’s test for equal variances

To interpret Jamovi results, say _

which test used, if significant, effect size, assumptions met

Normality should be checked _

twice (before and after splitting groups)

Effect size tells _

practical importance of the finding

Statistical significance tells _

probability that result occurred by chance

A Type I error occurs when _

null is true but rejected = we found a false effect

A Type II error occurs when _

null is false but not rejected = we missed the real effect

Increasing sample size _

reduces sampling error and increases power

Jamovi output shows a variable measured twice for the same people → use _ test

paired/dependent samples t test

Jamovi output shows two unrelated groups (e.g., male vs female) → use _ test

independent samples t test

Jamovi output compares one sample mean to a known μ = 50 → use _ test

single sample t test

If Jamovi output includes “Levene’s Test for Equality of Variances” → assumption being checked = _

homogeneity of variance

If Levene’s p > .05 → variances are _ and the regular t test row is used

equal

If Levene’s p < .05 → variances are _ and Welch’s t row is used

unequal

Jamovi output with p = 0.021 (< .05) → result is _

statistically significant (reject H₀)

Jamovi output with p = 0.41 (> .05) → result is _

not significant (fail to reject H₀)

If CI includes 0 → effect is _

not significant

If CI does not include 0 → effect is _

statistically significant

Jamovi shows Cohen’s d = 0.18 → effect size is _

small

Jamovi shows Cohen’s d = 0.55 → effect size is _

medium

Jamovi shows Cohen’s d = 0.95 → effect size is _

large

If the research design measures the same participants twice (pre/post) → use _

dependent samples t test

If comparing two separate classes or groups → use _

independent samples t test

If comparing one sample to a population mean → use _

single sample t test

To calculate t in Jamovi → the software first computes _ and then divides the mean difference by it

standard error

Interpretation of df = 37 means _ were used in a single/dependent test

38 participants (N – 1 = 37)

Interpretation of df = 22 in an independent test → total participants = _

24 (N₁ + N₂ – 2 = 22)

In Jamovi, choose “Paired Samples t-test” when _

each row represents the same person’s two scores

In Jamovi, choose “Independent Samples t-test” when _

grouping variable separates different people

In Jamovi, choose “One Sample t-test” when _

testing a sample mean against a fixed value

If data violates normality slightly but n > 30 → _ still applies because of the Central Limit Theorem

t test

Jamovi output reports 95 % CI [ 2.3, 5.7 ] → we are _ % confident true mean difference lies between those values

95

Jamovi APA result example → t(38) = 6.58, p < .05 means _

null rejected → significant difference

If t(24) = 1.32, p = .19 → _ the null hypothesis

fail to reject

When reporting in APA, t and p are always and

italicized, lowercase

When normality and homogeneity assumptions are met → results are _

valid and trustworthy

When assumptions are violated → interpret results with _

caution

Jamovi Question → Which test uses Levene’s? Answer = _

independent samples t test

Jamovi Question → Which test checks normality of difference scores? Answer = _

dependent samples t test

Jamovi Question → Which test uses only one column of data vs known mean? Answer = _

single sample t test