Mean and Mean difference are the two key statistical measures used in the statistical analysis

Meta-analysis is one of the methods of systematic review

 The term "effect size" is commonly used in the social sciences, especially in meta-analysis. An effect size is a value that shows the magnitude

SMDs, often referred to as effect sizes, can be interpreted using commonly accepted guidelines, primarily derived from social science research.

Review authors may consider including a rule of thumb in the Comments column of the ‘Summary of Findings

The real-world importance of an effect size is highly context-dependent, meaning that a small effect in one setting may still be clinically significant

This is derived from a Z test to determine if there is no effect (or no effect

Generally, P value below 0.05 is referred statistically significant, states it is small adequate to reject

The P value refers the probability of getting the observed effect (stronger effect) under

Statistical significance explains if there is a treatment effect exists or not while the effect size shows

P values depend on both the effect estimate and the sample size (or the precision of the effect estimate).

A P value higher than 0.05 does not prove that an intervention has no observed effect.

Cochrane does not support using terms like “statistically insignificant” due to the chance for misinterpretation

Confidence intervals (CIs) refer the range within which the intervention effect is likely lies, indicating a magnitude of uncertainty around

A confidence interval is an interval that gives a range for observed data within which the population

A 95% confidence interval (CI) means that if the study were repeated multiple times

The sample size in a study significantly affects the width of the confidence interval

Random-effects models are used for estimating the heterogeneity across studies, indicating the average effect across

Confidence intervals can be reported at different confidence levels (e.g., 90%, 95%, 99%)

There is a strong relationship between confidence intervals P values and confidence intervals

Confidence intervals can be used to determine the significance of an intervention for the intended effect.

Each confidence levels can indicates different degrees of certainty

If an effect estimate is small and its confidence interval excludes the minimum clinically meaningful benefit

A funnel plot is a scatter plot that visually represents the effect estimates from individual studies against

Funnel charts are scatter plots that display treatment effect estimates from individual studies against a measure

No, Publication bias will not always show asymmetry in meta-analysis. If no true intervention effect

Standard error is preferred because it accounts for additional factors influencing statistical power

Study precision is plotted on the vertical axis in funnel plot (reversed scale).

The studies with negative result will be ingnored for reporting in a funnel plot

The term “funnel plot” comes from the fact that as study size increases, the precision of the estimated

Assume an industrial company decides to start a project in a village. This would require the village

Ratio measures (odds ratios, risk ratios) → Should be plotted on a logarithmic scale to ensure symmetry.

 Inability to confirm bias – In some cases funnel plots states bias but do not confirm it; further statistical interpretations are required to confirm it.

Safety is the detection, evaluation, understanding, and prevention of adverse effects from

A hypothesis is essential to scientific research, shaping the research goal and supporting the approach

Obtaining the Standard Deviation for change scores need correlation formula. But the change score

f homogeneous variance is assumed, the Cohen method can be used to pool SD (Formula 15).

  If only the IQR is provided, estimation follows Sub-circumstance 3.2 (Formula 12).

If a study provides the range but does not specify minimum and maximum values, the formula used ..

Lou et al. stated a alteration of Bland’s method to calculate Standard Deviation based...