Podcast
Questions and Answers
Which of the following describes the type of distribution used for parametric tests?
Which of the following describes the type of distribution used for parametric tests?
- Uniform distribution
- Normal distribution (correct)
- Skewed distribution
- Exponential distribution
What does the term 'p-value' generally indicate in hypothesis testing?
What does the term 'p-value' generally indicate in hypothesis testing?
- The level of significance set by the researcher
- The confidence level of the sample mean
- The probability of obtaining results as extreme as the observed results under the null hypothesis (correct)
- The probability of the null hypothesis being true
In the context of non-parametric tests, which correlation measure would be appropriate?
In the context of non-parametric tests, which correlation measure would be appropriate?
- Kendall correlation
- Pearson correlation
- Chi-squared correlation
- Spearman correlation (correct)
What is the significance of an increasing alpha level in hypothesis testing?
What is the significance of an increasing alpha level in hypothesis testing?
What is the formula for calculating a 95% confidence interval for the sample mean?
What is the formula for calculating a 95% confidence interval for the sample mean?
Flashcards
Parametric tests
Parametric tests
Statistical tests that assume the data follows a specific distribution (often normal).
Non-parametric tests
Non-parametric tests
Statistical tests that don't assume a specific data distribution.
Spearman correlation
Spearman correlation
A non-parametric measure of the relationship between two variables.
P-value
P-value
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Increasing alpha
Increasing alpha
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Study Notes
Descriptives
- Data summaries for various variables (e.g., serum, diet) are presented, including means, standard errors, confidence intervals, trimmed means, medians, variances, standard deviations, minimums, maximums, ranges, interquartile ranges, skewness, and kurtosis.
Tests of Normality
- Kolmogorov-Smirnov and Shapiro-Wilk tests were used to assess normality for serum across different dietary groups.
- Significance levels (p-values) provided from these tests show the likelihood of a deviation from a normal distribution.
Tests of Homogeneity of Variances
- Levene's test assessed whether variances are equal across groups (e.g., diets).
- Separate statistics (based on means, medians, and trimmed means) were calculated, and the corresponding significance levels are provided.
ANOVA
- Analysis of variance (ANOVA) to compare means across dietary groups, yielding an F-statistic, degrees of freedom, mean squares, and the significance level for the between-groups effect.
- Within and total sums of squares are also displayed.
Scatter Plots and Correlation
- Scatter plots can be used to visualize the relationship between bivariate data.
- Correlation coefficients (e.g., Pearson's r) measure the linear relationship, with a positive value indicating that as one variable increases, the other tends to increase as well.
Parametric vs. Non-parametric Tests
- Different statistical tests (e.g., t-tests, ANOVA) are appropriate depending on whether the data meets assumptions of normality and equal variance.
- Non-parametric alternatives (e.g., Mann-Whitney, Kruskal-Wallis) can be used when these assumptions are not met.
Multiple Comparisons
- Comparisons of diets (A, B, C, D) against a control group were made.
- Significance levels (p-values) given for these multiple comparisons indicate any statistically significant differences across groups under investigation.
- Confidence intervals calculated for mean differences.
Homogeneous Subsets
- Groups that have similar data are grouped together.
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