Equivalence trials aim to show that two treatments are therapeutically equivalent, typically comparing a new drug with an existing one. However, the principles for designing, conducting, and analyzing these trials are not well understood, leading to issues such as insufficient patient numbers and design biases. The use of hypothesis testing can also lead to inappropriate conclusions. The design of equivalence trials should mirror that of active comparator trials, minimizing patient losses and using confidence intervals for analysis. The gold standard in clinical research is the randomized placebo-controlled double-blind trial, but this is not always ethical. In such cases, an existing drug is used as an active comparator. Equivalence trials are often used to show that a new treatment is as effective as an existing one, with possible advantages in safety, convenience, or cost. These trials require careful design and analysis to ensure reliable conclusions. Confidence intervals are crucial in equivalence trials, as they define a range for the possible true difference between treatments. If the confidence interval lies entirely within the predefined equivalence range, equivalence is demonstrated. The sample size calculation depends on the equivalence range (Δ), and the probabilities of type I and II errors (α and β). The choice of Δ requires input from clinical experts and is generally smaller than in comparative trials. The analysis of equivalence trials should include both intention-to-treat and per-protocol analyses. Intention-to-treat analysis includes all patients regardless of treatment adherence, while per-protocol analysis includes only those who followed the protocol. Both approaches are important to ensure the validity of the trial results. The design and conduct of equivalence trials must be rigorous to ensure internal validity. This includes careful selection of inclusion and exclusion criteria, appropriate dosing regimens, and thorough analysis of patient compliance and response. The results of the trial should provide evidence of the efficacy of both treatments, and the trial should be conducted in a way that minimizes biases towards a conclusion of no difference. The paper emphasizes the importance of rigorous methods in equivalence trials to ensure reliable conclusions. It provides formulas for calculating sample size and power for both normally distributed and binary data, and highlights the need for careful analysis and interpretation of trial results. The use of confidence intervals and appropriate statistical methods is essential to determine equivalence. The paper also discusses the importance of transparency and critical evaluation in the reporting of equivalence trials to avoid incorrect conclusions.Equivalence trials aim to show that two treatments are therapeutically equivalent, typically comparing a new drug with an existing one. However, the principles for designing, conducting, and analyzing these trials are not well understood, leading to issues such as insufficient patient numbers and design biases. The use of hypothesis testing can also lead to inappropriate conclusions. The design of equivalence trials should mirror that of active comparator trials, minimizing patient losses and using confidence intervals for analysis. The gold standard in clinical research is the randomized placebo-controlled double-blind trial, but this is not always ethical. In such cases, an existing drug is used as an active comparator. Equivalence trials are often used to show that a new treatment is as effective as an existing one, with possible advantages in safety, convenience, or cost. These trials require careful design and analysis to ensure reliable conclusions. Confidence intervals are crucial in equivalence trials, as they define a range for the possible true difference between treatments. If the confidence interval lies entirely within the predefined equivalence range, equivalence is demonstrated. The sample size calculation depends on the equivalence range (Δ), and the probabilities of type I and II errors (α and β). The choice of Δ requires input from clinical experts and is generally smaller than in comparative trials. The analysis of equivalence trials should include both intention-to-treat and per-protocol analyses. Intention-to-treat analysis includes all patients regardless of treatment adherence, while per-protocol analysis includes only those who followed the protocol. Both approaches are important to ensure the validity of the trial results. The design and conduct of equivalence trials must be rigorous to ensure internal validity. This includes careful selection of inclusion and exclusion criteria, appropriate dosing regimens, and thorough analysis of patient compliance and response. The results of the trial should provide evidence of the efficacy of both treatments, and the trial should be conducted in a way that minimizes biases towards a conclusion of no difference. The paper emphasizes the importance of rigorous methods in equivalence trials to ensure reliable conclusions. It provides formulas for calculating sample size and power for both normally distributed and binary data, and highlights the need for careful analysis and interpretation of trial results. The use of confidence intervals and appropriate statistical methods is essential to determine equivalence. The paper also discusses the importance of transparency and critical evaluation in the reporting of equivalence trials to avoid incorrect conclusions.