Test a claim about a population mean (one sample) or compare two means, when the population is roughly normal.
In short: A t-test for a mean checks whether a sample mean differs significantly from a claimed population mean when the standard deviation is estimated from the sample. Enter the sample data or its summary values, choose the tail and read the t statistic and the p-value. Compare the p-value with the significance level to conclude.
| TI-84 Plus CE | Casio fx-CG50 and fx-CG100 | TI-Nspire CX | |
|---|---|---|---|
| Key sequence | STAT → TESTS → T-Test (one sample) or 2-SampTTest; enter the hypothesised mean and either the data list or the summary stats. | Main menu → Statistics → TEST → t → 1-SAMPLE (or 2-SAMPLE). | menu → Statistics → Stat Tests → t Test (or 2-Sample t Test). |
Tip: Match the tail (≠, <, >) to H₁. Read off t and the p-value, then write your conclusion in context comparing p to the significance level.
Tip: Match the tail (≠, <, >) to H₁. Read off t and the p-value, then write your conclusion in context comparing p to the significance level.
Tip: Match the tail (≠, <, >) to H₁. Read off t and the p-value, then write your conclusion in context comparing p to the significance level.
Here's a real IB-style question that uses exactly this technique.
A sample of 15 values has mean 54 and standard deviation 6. Test H₀: μ = 50 against H₁: μ ≠ 50 at the 5% significance level.
Test a claim about a population mean (one sample) or compare two means, when the population is roughly normal. Testing a claim about a single population mean, when the population standard deviation is unknown. The sample is roughly normal (or reasonably large) but you only have the sample's own standard deviation.
1. State H₀, H₁ (one- or two-tailed) and the significance level. 2. STAT → TESTS → T-Test (one sample) or 2-SampTTest; enter the hypothesised mean and either the data list or the summary stats.
1. State H₀, H₁ (one- or two-tailed) and the significance level. 2. Main menu → Statistics → TEST → t → 1-SAMPLE (or 2-SAMPLE).
1. State H₀, H₁ (one- or two-tailed) and the significance level. 2. menu → Statistics → Stat Tests → t Test (or 2-Sample t Test).
Match the tail (≠, <, >) to H₁. Read off t and the p-value, then write your conclusion in context comparing p to the significance level.
In the worked example on this page (4 marks, Paper 2), the marks are: M1: t = (54 − 50) ÷ (6/√15); A1: t = 2.58, p = 0.0217; R1: p < 0.05, so reject H₀: evidence μ ≠ 50.
Questions that need this technique link back to this guide. Try one in practice mode, or see every GDC guide.