T STUDENT PARA UNA MUESTRA | Explicación, cómo hacerlo, interpretarlo y reportarlo en SPSS
Understanding the Student T-Test
Introduction to the Student T-Test
- The student t statistic is used for hypothesis testing to determine if the mean score of a sample differs from a known population value.
- It allows comparison between a specific sample's mean and an established population mean, such as IQ scores.
Hypothesis Testing Framework
- The null hypothesis posits no significant difference between the sample and population means, while the alternative hypothesis suggests there is a difference.
- Acceptance of the null hypothesis occurs when p-value > 0.05; rejection happens when p-value < 0.05.
Requirements for Conducting a T-Test
- Key requirements include:
- The variable must be numerical or quantitative.
- Measures should be independent (not related).
- Data must come from a random sample.
- Dependent variable data should follow a normal distribution.
Performing the Analysis in SPSS
- An example involves comparing IQ scores of children aged 3 to 6 against a population average of 100 points using SPSS software.
- In SPSS, input dependent variables and set test values (population mean), with options for confidence intervals available but not necessary for this analysis.
Interpreting Results
- Output includes descriptive statistics like means (110.44) and standard deviations (15.09), indicating higher average IQ in the sample compared to the normative group.
- A p-value less than 0.05 leads to rejecting the null hypothesis, confirming that this sample has significantly higher IQ scores than expected.
Reporting Findings According to APA Standards
- Reports should include:
- Sample mean and standard deviation,
- t-test value,
- Degrees of freedom,
- P-value formatted correctly according to APA guidelines (e.g., reporting p < .001 instead of p = .000).
Considerations for Future Comparisons