It is the probability of observing the result, or something more extreme, assuming the null hypothesis is true. It helps determine whether the result is statistically significant and whether to reject the null hypothesis. A small p-value (typically < 0.05) suggests the difference or effect is likely real and not due to chance.
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Analysis of Variance Before-After Test Chi-Square Test Chi-Square Test of Independence Conceptual Framework Confounding Variable Construct Contingency Table Correlation Analysis Dependent Sample 't' Test Descriptive Statistical Analysis Distribution-Free Test EFA Exploratory Factor Analysis Factor Analysis Formulation of Research Problem Independent Sample 't' Test Latent Variable Linear Regression MANOVA Matched Paired Sample 't' test Mean Analysis Measures of Central Tendency Multi-Variate Analysis Multi-Variate Analysis of Variance Multiple ANOVA Non-Parametric Test One-Way ANOVA One Sample t-Test Paired Sample 't' Test Pearson Problem Statement Relationship Repeated Measures ANOVA Research Model Research Objectives Research Questions Sample Size Simple Sample 't' Test Small Sample Test Spearman Theoretical Framework T Test Two-Way ANOVA Variation