- By
**mandy** - Follow User

- 119 Views
- Uploaded on

Download Presentation
## PowerPoint Slideshow about 'Overview of Statistical Tests Available' - mandy

**An Image/Link below is provided (as is) to download presentation**

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript

Deciding Which Test to Use

- Is DV meristic, mensural, or categorical?
- Is IV continuous or discrete in its variation?
- If IV is discrete, are two groups being compared, or more than two?
- Are there multiple IVs?

No-difference H0 or no-correlation H0?Is DV meristic/mensural or categorical?

Student’s t-Test

- Parametric test
- Compare two independent samples
- One version for equal variances
- Less powerful version for unequal variances
- H0: There is no difference in [DV] between two groups or treatments [IV]

Paired t-test

- Parametric
- Data paired rather than drawn from independent populations
- Assumes equal variances

Mann-Whitney U-test

- aka Rank-Sum Test
- Nonparametric comparison of two samples of meristic or mensural DV

z-test

- aka Binomial Test of Proportions
- Two samples
- DV occurs in two categories for each sample
- H0: There is no difference in binomial outcomes [DV] between two samples [IV]

Chi-square (χ2) Tests

- χ2 Goodness-of-Fit Test
- Sample distribution vs. hypothesized/theoretical distribution
- χ2 Test for Independence
- Sample distribution vs. sample distribution
- Mantel-Haenszelχ2 Test for Independence
- Compares distributions of samples with two or more IVs that may influence DV distribution

One-way Analysis of Variance (ANOVA)

- Parametric
- H0: There is no difference in [DV] among three or more groups or treatments [IV]
- Rejection of H0 requires post hoc testing to identify which samples differ significantly from which other samples
- Fisher’s Least Significant Difference (LSD) test
- Tukey’s test
- Student-Newman-Keuls test
- Bonferroni t-tests
- Duncan’s Multiple Range test
- Sheffé comparisons

HANDOUTGodfrey and Bryant, 2000European Robin Energy Expenditure

Note: For both tests, post hoc Scheffé comparisons show CHILLED was significantly

greater than other two treatments, which did not differ from one another.

Kruskal-Wallis Test

- Nonparametric equivalent of ANOVA
- Rejection of H0 requires nonparametric analog of post hoc testing
- Dunn’s Multiple Comparisons test

Two-Way (or More) ANOVA

- Two IVs analyzed simultaneously
- Multiple H0s:
- No difference in [DV] between/among groups as established by IV1
- No difference in [DV] between/among groups as established by IV2
- No difference in [DV] attributable to the interaction of IV1 and IV2
- More complex models for three or more IVs

Correlation Analysis

- H0: There is no correlation of DV with IV
- Positive correlation: Direct relationship
- Negative correlation: Indirect relationship
- No correlation: Variation in DV not related to variation in IV

Simple Linear Regression

- If they are correlated, fit DV and IV data to a best-fitting line

y = ax + b

DV = a(IV) + b

a = slope

b = y-intercept

Multiple Linear Regression

- >1 continuous IV
- Multidimensional best-fitting line:

DV = a(IV1) + b(IV2) + c(IV3) … + z

(ac are slopes in separate dimensions; z is intercept)

- With each added IV, partial correlation coefficient assesses whether it significantly improves fit to DV
- H0: There is no correlation of DV with IVx, after correction for correlation of DV with other IVs in model

Analysis of Covariance

- Two IVs: IVc continuous, IVd discrete
- Typically, IVc regarded as a confounding variable, known/suspected to be correlated with DV
- H0: No difference in DV between/among treatments or groups (IVd), after correction for correlation of DV with IVc.

The General Linear Model (GLM)

- Mix of multiple linear regression (continuous IVs) and ANCOVA (allows for discrete IVs)
- Number of IVs limited only by sample size
- H0 for each IV states that its variation is not significantly related to variation in DV (given relationship of other IVs to DV)
- Interaction of IVs can also be examined in additional H0s

Logistic Regression

- IV is continuous, but DV is discrete and categorical
- H0: Probability of categorization of DV is not correlated with IV

Multivariate Analyses

- Variety of techniques that take large data sets with numerous variables and…
- …allow relatively simple graphical representations
- …identify interrelationships of variables

Phylogenetic Comparative Methods

- Used for analyzing data from related species
- Necessary because phylogenetic relationships cause violation of the assumption of independence of data points
- Primarily used for correlation analysis, but can be adapted for use in other types of analysis

Monte Carlo Randomizations

- Uses 100s to 1000s of randomizations of the sample data to create a null distribution for the H0
- Parametric assumption thus not necessary
- Used to create highly specific H0s for particular scenarios one may wish to analyze

Download Presentation

Connecting to Server..