Definition and Types of Research Hypothesis
📌 A hypothesis is a temporary answer or tentative guess made before an experiment, based on existing literature reviews and prior research.
🧪 Research hypotheses often contain neutral or generally accepted statements that aim to answer the specific research questions or problems formulated in a proposal.
📚 In statistical inferential analysis, two types of hypotheses must be tested: the research hypothesis (testing the tentative answer on the sample) and the statistical hypothesis (testing if the sample results apply to the broader population).

Types of Research Hypotheses
📌 Descriptive Hypothesis: Concerns a single, standalone variable (e.g., "I am handsome").
📊 Comparative Hypothesis: Involves a comparison or contrast between two research variables (e.g., comparing intelligence between men and women, or Class A vs. Class B performance).
🔗 Associative Hypothesis: Examines the relationship or association between two variables (e.g., the relationship between motivation and achievement, or religiosity and corruption behavior).

Statistical Hypothesis Formulation (H₀ and H₁)
📌 Statistical hypotheses include the Null Hypothesis ($\text{H}_0$) and the Alternative Hypothesis ($\text{H}_1$ or $\text{H}_\text{a}$), which are always contradictory; $\text{H}_0$ always contains an equality sign (e.g., $=$, $\le$, or $\ge$).
⬅️ One-Tailed Hypothesis (Right/Greater Than): Rejection area for $\text{H}_0$ is only on the right side of the distribution curve (e.g., $\mu_1 > \mu_2$).
➡️ One-Tailed Hypothesis (Left/Less Than): Rejection area for $\text{H}_0$ is only on the left side of the distribution curve (e.g., $\mu_1 < \mu_2$).
↔️ Two-Tailed Hypothesis (Difference): Rejection area for $\text{H}_0$ exists on both the left and right sides (e.g., $\mu_1 \neq \mu_2$), typically used when seeking to determine *if* a difference exists, not the direction.

Hypothesis Testing in Inferential Statistics
📌 Hypothesis testing involves using inferential statistics (parametric or non-parametric) to decide whether to accept $\text{H}_0$ or reject $\text{H}_0$ based on sample data.
🔬 Parametric Difference Tests include procedures for one sample (Z-test for $n \ge 30$, T-test for $n < 30$), two dependent samples (T-test), two independent samples (Z-test if $n \ge 30$ for each group, T-test if $n < 30$ requiring variance homogeneity check), and more than two independent samples (F-test or ANOVA).

Key Points & Insights
➡️ The core purpose of a hypothesis is to provide a testable, temporary answer before the main research execution.
➡️ For statistical hypotheses, $\text{H}_0$ must always include an element of equality (e.g., $\mu_1 = \mu_2$), while $\text{H}_1$ expresses the research claim (e.g., $\mu_1 > \mu_2$ or $\mu_1 \neq \mu_2$).
➡️ Determining the type of statistical test (one-tailed vs. two-tailed) depends entirely on the directionality stated in the alternative hypothesis ($\text{H}_1$).
➡️ If $\text{H}_1$ suggests a specific direction (e.g., "better than"), it's a one-tailed test; if it only suggests inequality (e.g., "is there a difference?"), it's a two-tailed test.

📸 Video summarized with SummaryTube.com on Dec 31, 2025, 11:31 UTC