# Category:Tests Of Statistical Significance

Related Online Training modules | |
---|---|

Automatic Tests of Statistical Significance | |

Significance Tests on Grids | |

Type 1 Error | |

Population Weights | |

Non-Proportional Sampling Weights | |

Planned Tests of Statistical Significance | |

Column Comparisons | |

ANOVA with Post Hocs | |

ANOVA Repeated Measures with Post Hocs | |

Generally it is best to access online training from within Q by selecting Help : Online Training |

## Contents

## Basic mechanics of Q's testing

The basic process of statistical testing in Q works as follows:

- The user specifies the relationships to be tested.
- Q computes a p-Value
- Results are highlighted as significant if
- The Corrected p-Value is less than or equal to the specified Overall significance level.
- The relevant settings are set to show significance (see Ways of Showing Statistical Significance).

- If a Rule has been applied, this is then used and may replace the results computed in the previous steps.

See Overview of Statistical Testing in Q for more detail.

## Reading Tables and Interpreting Significance Tests

Basic tips for interpreting most tables can be obtained by selecting **Help** and Interpret This Table.

Links to explanations of how to read the most common tables and their significance tests can be found here.

## Statistical tests categorized by data type

- One Sample Tests - Proportions
- One Sample Tests - Means
- Independent Sample Tests - Comparing Two Proportions
- Independent Sample Tests - Comparing Two Means
- Related Samples Tests - Comparing Two Proportions
- Related Samples Tests - Comparing Two Means
- Correlations - Comparing Two Numeric Variables
- ANOVA-Type Tests - Comparing Three or More Groups
- Statistical Tests for Experiment Questions
- Statistical Tests for Ranking Questions
- Multivariate Tests
- Other Tests

## Working out which test has been conducted

To work out which test has been conducted on the cell of a table:

- Check to see if any Rules have been applied. If they have, review their documentation.
- Select that cell and press (see Planned Tests of Statistical Significance).

## See also

SurveyAnalysis.org contains a general discussion about the use and interpretation of tests of statistical significance (see The Role of Statistical Tests in Survey Analysis).

Further reading: Market Research Analysis Software

## Subcategories

This category has the following 2 subcategories, out of 2 total.

## Pages in category ‘Tests Of Statistical Significance’

The following 134 pages are in this category, out of 134 total.

### B

### C

- Cell Comparisons
- Chi-Square Test for Compatibility of K Counts
- Chi-Square Test of a Frequency Distribution
- Cochran's Q
- Column Comparisons with Missing Data and Grid Questions
- Combined p-Values
- Common Questions About Statistical Testing in Q
- Comparing Columns Across Spans
- Complex Samples Dependent T-Test - Comparing a Sub-Group Mean to Total
- Complex Samples Dependent T-Test - Comparing a Sub-Group Proportion to Total
- Complex Samples Dependent Z-Test - Comparing a Sub-Group Mean to Total
- Complex Samples Dependent Z-Test - Comparing a Sub-Group Proportion to Total
- Complex Samples t-Test of a Mean
- Complex Samples t-Test of a Proportion
- Complex Samples Z-Test of a Mean
- Complex Samples Z-Test of a Proportion
- Confidence Interval
- Correlations - Comparing Two Numeric Variables
- Crosstabs of Proportions (One Categorical Question by Another Categorical Question)

### D

### F

### H

- How Q Highlights Results as Being Significant
- How to Change Significance Levels for Column Comparisons
- How To Compare A Sub-Group Against The Total
- How to Conduct MANOVA Tests
- How to Conduct One-Tailed Column Comparisons
- How To Create Banner Tables Using Pick One - Multi Or Grid Questions
- How to Include the Main NET Column in Column Comparisons
- How To Override Default Statistical Testing Settings
- How to Replicate SPSS Significance Tests in Q
- How to Replicate Survey Reporter Significance Tests
- How to Specify Columns to be Compared
- How to Specify Comparisons for ANOVA-Based Tests
- How To Test Against The NET/Total/Average
- How To Test Between Adjacent Time Periods

### I

- Independent Complex Samples t-Test - Comparing Two Means
- Independent Complex Samples T-Test - Comparing Two Proportions
- Independent Complex Samples Z-Test - Comparing Two Means
- Independent Complex Samples Z-Test - Comparing Two Proportions
- Independent Sample Tests - Comparing Two Means
- Independent Sample Tests - Comparing Two Proportions
- Independent Samples - Quantum Column Means Test
- Independent Samples - Quantum Column Proportions Test
- Independent Samples - Survey Reporter Column Means Test
- Independent Samples - Survey Reporter Column Proportions Test
- Independent Samples t-Test - Comparing Two Coefficients
- Independent Samples t-Test - Comparing Two Means with Equal Variances
- Independent Samples t-Test - Comparing Two Means with Unequal Variances
- Independent Samples t-Test - Comparing Two Probability %
- Independent Samples T-Test - Comparing Two Proportions
- Independent Samples T-Test - Equal Variance
- Independent Samples T-Test - Pooled Variance
- Independent Samples T-Test - Unequal Variance
- Independent Samples Z-Test - Comparing Two Means with Equal Variances
- Independent Samples Z-Test - Comparing Two Means with Unequal Variances
- Independent Samples Z-Test - Comparing Two Proportions
- Independent Samples Z-Test - Comparing Two Proportions (Pooled)
- Independent Samples Z-Test - Comparing Two Proportions (Un-Pooled)
- Information Criteria
- Interpretation of the Overall Significance Level by Q
- Interpreting Column Comparisons

### M

- Mann–Whitney U
- Modifying Significance Tests
- Modifying Significance Tests Using Rules
- Multiple Comparison Correction
- Multiple Comparisons (Post Hoc Testing)
- Multiple Comparisons t-Test (Fisher LSD)
- Multiple Comparisons t-Test with Bonferroni Correction
- Multiple Comparisons t-Test with False Discovery Rate Correction
- Multivariate Tests

### O

### P

- P-Values
- Paired t-Test of Means
- Paired t-Test of Proportions
- Paired Z-Test of Means
- Paired Z-Test of Proportions
- Pearson's Chi-Square Test of Independence
- Pearson's Product Moment Correlation
- Pearson’s Chi-square for Canonical Correlation Analysis
- Planned ANOVA-Type Tests
- Planned Tests Of Statistical Significance

### R

- Related Samples Tests - Comparing Two Means
- Related Samples Tests - Comparing Two Proportions
- Repeated Measures
- Repeated Measures ANOVA
- Repeated Measures ANOVA with Greenhouse & Geisser Epsilon Correction
- Repeated Measures ANOVA with Huynh and Feldt Epsilon Correction
- Repeated Measures ANOVA with Lower Bound Epsilon Correction

### S

- Second Order Rao-Scott Test of Independence of a Contingency Table
- Setting Statistical Assumptions When Setting Up Projects
- Significance Tests Change When I Merge, Hide or Remove Categories
- Significance Tests on Trend Plots
- Simplified Independent Complex Samples T-Test - Comparing Two Means
- Simplified Independent Complex Samples T-Test - Comparing Two Proportions
- Spearman’s Correlation
- Standard Errors in an Experiment or Ranking Question Are Large or NaN
- Statistical Assumptions
- Statistical Tests for Experiment Questions
- Statistical Tests for Ranking Questions