Related Experiment Video
Updated: Apr 17, 2026
03:37
Evaluating the Effects of Different Polishing Methods on Color Stability of Dental Restorations in Pediatric Dentistry
Published on: June 6, 2025
620
Statistical testing against baseline was common in dental research.
Despina Koletsi1, Arun Madahar2, Padhraig S Fleming2
1Department of Orthodontics, School of Dentistry, University of Athens, 2 Thivon St, 11527 Goudi, Athens, Greece.
Journal of Clinical Epidemiology
|February 10, 2015
Summary
Many dental studies use baseline comparisons, but their interpretation needs improvement. Enhanced statistical reporting is crucial for accurate research findings in dentistry.
Area of Science:
- Dental research methodology
- Biostatistics in clinical studies
Background:
- Within-group comparisons to baseline are frequently employed in dental research.
- The appropriate conduct and interpretation of these statistical analyses are critical for valid research outcomes.
Purpose of the Study:
- To evaluate the prevalence of within-group comparisons with baseline in leading dental journals.
- To investigate associations between the use of baseline comparisons and study characteristics like journal and design.
Main Methods:
- Electronic search of 30 consecutive issues from five prominent dental journals.
- Assessment of statistical analysis conduct, reporting, and interpretation, focusing on baseline comparisons.
- Descriptive statistics and chi-square/Fisher's exact tests to analyze associations.
Main Results:
- 184 studies were analyzed; 43 (23%) used baseline comparisons for outcome interpretation.
- The Journal of Endodontics published the highest proportion of studies (46%).
- Inappropriate baseline testing was less frequent in interventional studies (P < 0.001).
Conclusions:
- Comparisons with baseline are common in both observational and interventional dental research.
- Improved statistical conduct and reporting are necessary for appropriate and informative research inferences.
Related Concept Videos
01:16Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data
652
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
652
01:19Testing a Claim about Standard Deviation
3.3K
A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
3.3K
01:16Statistical Hypothesis Testing
7.2K
Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...
7.2K
01:09Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test
7.1K
In parametric statistics, two fundamental tests stand out for their utility and wide application: the Student's t-test and goodness-of-fit tests. These tests provide researchers with a robust method for drawing insights from data, testing hypotheses, and making informed decisions based on their findings.
The Student's t-test is a statistical test that examines if there is a statistically significant difference between the means of two groups. This test is instrumental when dealing with...
The Student's t-test is a statistical test that examines if there is a statistically significant difference between the means of two groups. This test is instrumental when dealing with...
7.1K
01:52Regression Toward the Mean
7.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
7.3K
01:10Bonferroni Test
3.6K
The Bonferroni test is a statistical test named after Carlo Emilio Bonferroni, an Italian mathematician best known for Bonferroni inequalities. This statistical test is a type of multiple comparison test to determine which means are different than the rest. Bonferroni test can minimize the Type 1 error by reducing the significance level alpha, which otherwise increases with sample pairs.
The means of different samples are first paired in all possible combinations.
The null hypothesis of the...
The means of different samples are first paired in all possible combinations.
The null hypothesis of the...
3.6K