Inferential Statistics
- Inferential Statistics – Definition, Types, Examples, Formulas
- Observational Studies and Experiments
- Sample and Population
- Sampling Bias
- Sampling Methods
- Research Study Design
- Population Distribution, Sample Distribution and Sampling Distribution
- Central Limit Theorem
- Point Estimates
- Confidence Intervals
- Introduction to Bootstrapping
- Bootstrap Confidence Interval
- Paired Samples
- Impact of Sample Size on Confidence Intervals
- Introduction to Hypothesis Testing
- Writing Hypotheses
- Hypotheses Test Examples
- Randomization Procedures
- p-values
- Type I and Type II Errors
- P-value Significance Level
- Issues with Multiple Testing
- Confidence Intervals and Hypothesis Testing
- Inference for One Sample
- Inference for Two Samples
- One-Way ANOVA
- Two-Way ANOVA
- Chi-Square Tests
Randomization Procedures
Randomization in Statistics and Experimental Design
What is Randomization?
Randomization procedures are used in experimental research to randomly assign participants to different groups or conditions. This helps to ensure that the groups are equivalent in terms of any potential confounding variables, and that any observed differences between groups can be attributed to the treatment or intervention being studied.
Like bootstrapping procedures, randomization procedures use resampling techniques to construct a sampling distribution that can be used to make inferences about the population. What makes a randomization distribution different is that it is constructed given that the null hypothesis is true. The randomization distribution will be centered on the value in the null hypothesis.
For example, below are some steps that can be used to conduct hypothesis test:
- Determine what type of test you need to conduct and write the hypotheses.
- Construct a randomization distribution under the assumption that the null hypothesis is true.
- Use the randomization distribution to find the p-value.
- Decide if you should reject or fail to reject the null hypothesis.
- State a real-world conclusion in relation to the original research question.
Randomization Techniques
There are several different types of randomization procedures that can be used, including:
1. Simple randomization: Participants are randomly assigned to different groups without any restrictions or stratification.
2. Stratified randomization: Participants are divided into subgroups based on specific criteria, such as age or gender, and then randomly assigned to different groups within each subgroup.
3. Block randomization: Participants are grouped into blocks of a certain size, and then randomly assigned to different groups within each block. This helps to ensure that the groups are balanced in terms of the number of participants in each group.
4. Cluster randomization: Instead of randomizing individual participants, entire groups or clusters are randomly assigned to different groups or conditions. This is often used in settings where it is not possible or practical to randomize individuals, such as in schools or hospitals.
5. Adaptive randomization: This approach uses algorithms to adjust the allocation of participants to different groups based on the observed data, with the goal of maximizing the statistical power of the study.
Regardless of the specific approach used, the goal of randomization is to ensure that any differences between groups are due to chance, rather than systematic differences in participant characteristics or other factors. This helps to increase the validity and reliability of experimental research findings.