Determining sample size with the experts
You have your plan for performing data analysis. But do you know whether the collected sample is of adequate size to yield the desired result? The sample might be either too large or small, giving rise to type I & type II errors. This is exactly where the power analysis comes into your aid. It determines if the sample size is suitable and has the potential to test the given effect.
We understand the importance of power analysis and the hardships faced throughout the process. Our experienced statisticians take it upon themselves the task of performing power analysis and delivering the precise results.
Check for sample relevance
Irrespective of the type of your study, our statisticians check for the relevance of the sample collected. This is done by referring to the pilot study (if conducted) or relevant literature.
Determine statistical tests
Suitable statistical tests must be performed prior to the power analysis. Our team determines the most appropriate test based on the type of data being collected, and aimed at research objectives & also the effect size.
Perform pilot studies
Pilot study can be used as an effective approach to determine the sample size. We can help you perform the same using popular statistical tools.
Conduct priori & post-hoc analysis
We can also perform a beforehand power analysis test (a priori) and afterhand analysis (post hoc).
Common Power Analysis Methods
The specific calculations can vary depending on the statistical test, design of the study, and assumptions made about the data. Our experts can help you select the most appropriate statistical software and guide you through the power analysis process, ensuring accurate and reliable results.
Z-test
For comparing means of two independent groups when the population standard deviation is known.
T-test
For comparing means of two independent groups when the population standard deviation is unknown.
ANOVA
For comparing means of multiple groups.
Chi-square test
For analyzing categorical data.
Correlation analysis
For measuring the strength of a linear relationship between two variables.
Regression analysis
For predicting a dependent variable based on one or more independent variables.
Frequently asked questions
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