Statistical Analysis and Hypothesis Testing

Comparing Sample Means

• The independent sample t-test is used to compare two sample means for continuous data.

Risk Factor and Disease Association

• The odds ratio summarizes the strength of the association between a risk factor and a disease.

Hypothesis Testing

• Step 3 in hypothesis testing involves specifying the null and alternative hypotheses.

Non-Parametric Tests

• The Mann-Whitney U test is the non-parametric equivalent to the independent sample t-test.

Bivariate Associations

• Scatter plots are used to investigate the bivariate associations with continuous variables.

Errors in Hypothesis Testing

• Type I error is related to rejecting a true null hypothesis.

Calculating Trimmed Mean in SPSS

• When calculating the 5% trimmed mean in SPSS, it removes 5% of the lowest and 5% of the highest values from the dataset.

Normality Testing

• Normality is tested in the output from Descriptive using the Shapiro-Wilk test.

Box Plot Interpretation

• The boxplot provided in the output shows the distribution of the data, including outliers and skewness.

Choosing Statistical Tests

• The choice of inferential statistical test depends on the distribution of the data, with parametric tests used for normal data and non-parametric tests used for non-normal data.

Comparing Group Means

• The independent sample t-test would be used to compare the BMI between two groups at baseline.

Significance Level

• The significance level is typically set at 0.05 (5%) for parametric tests.

Non-Parametric Tests for Group Comparison

• If the authors wanted to compare BMI between two groups at baseline using non-parametric tests, they should have used the Mann-Whitney U test.

Levene's Test

• Levene's test for Equality of Variances assesses whether the variances of the two groups are equal, which is an assumption of the independent samples t-test.