This article provides guidelines for selecting, calculating, and interpreting effect sizes (ESs) in research. ESs are essential for conveying the magnitude and direction of differences between groups or relationships between variables. They are crucial for understanding research findings beyond p-values, as p-values do not indicate effect magnitude. ESs help assess the overall contribution of a study and are required in many research reports, including the *Journal of Pediatric Psychology*. The article discusses various types of ESs, including standardized mean differences (SMDs), odds ratios (ORs), and product-moment correlations (r). SMDs are commonly used in group designs and are calculated using post-test means and standard deviations. ORs are useful for dichotomous outcomes, while r is used in correlational studies. ESs should be reported for all outcomes, regardless of p-values, and adjusted for pre-test differences when necessary. Calculating ESs requires attention to study design, sample size, and measurement methods. For example, small sample sizes can lead to large fluctuations in ESs, and adjustments may be needed for reliability and range restrictions. ESs should be interpreted in the context of prior research, practical significance, and clinical relevance. Adjusted ESs are important when pre-test differences may confound results. The article emphasizes the importance of reporting confidence intervals (CIs) around ESs to assess the precision of findings. ESs should not be interpreted solely based on their magnitude but also their practical or clinical significance. For instance, a small ES on a behavioral measure may have more clinical importance than a larger ES on an academic measure. Researchers should consider the type of outcome, study design, and measurement methods when choosing an appropriate ES. ESs are influenced by factors such as sample size, measurement reliability, and study rigor. Adjustments may be necessary for small samples or unreliable measures. The article also provides resources for calculating and interpreting ESs, including software and online tools. In conclusion, ESs are essential for understanding the magnitude and importance of research findings. They should be reported in all research studies and interpreted in the context of prior research, practical significance, and study design. Proper calculation and interpretation of ESs enhance the quality and utility of research findings.This article provides guidelines for selecting, calculating, and interpreting effect sizes (ESs) in research. ESs are essential for conveying the magnitude and direction of differences between groups or relationships between variables. They are crucial for understanding research findings beyond p-values, as p-values do not indicate effect magnitude. ESs help assess the overall contribution of a study and are required in many research reports, including the *Journal of Pediatric Psychology*. The article discusses various types of ESs, including standardized mean differences (SMDs), odds ratios (ORs), and product-moment correlations (r). SMDs are commonly used in group designs and are calculated using post-test means and standard deviations. ORs are useful for dichotomous outcomes, while r is used in correlational studies. ESs should be reported for all outcomes, regardless of p-values, and adjusted for pre-test differences when necessary. Calculating ESs requires attention to study design, sample size, and measurement methods. For example, small sample sizes can lead to large fluctuations in ESs, and adjustments may be needed for reliability and range restrictions. ESs should be interpreted in the context of prior research, practical significance, and clinical relevance. Adjusted ESs are important when pre-test differences may confound results. The article emphasizes the importance of reporting confidence intervals (CIs) around ESs to assess the precision of findings. ESs should not be interpreted solely based on their magnitude but also their practical or clinical significance. For instance, a small ES on a behavioral measure may have more clinical importance than a larger ES on an academic measure. Researchers should consider the type of outcome, study design, and measurement methods when choosing an appropriate ES. ESs are influenced by factors such as sample size, measurement reliability, and study rigor. Adjustments may be necessary for small samples or unreliable measures. The article also provides resources for calculating and interpreting ESs, including software and online tools. In conclusion, ESs are essential for understanding the magnitude and importance of research findings. They should be reported in all research studies and interpreted in the context of prior research, practical significance, and study design. Proper calculation and interpretation of ESs enhance the quality and utility of research findings.