HYPOTHESIS


I. What is Hypothesis Testing?

  • the statistical method applied in making decisions using experimental data.

  • testing an assumption made about the population.

II. What is Hypothesis?

  • proposed explanation, assertion or assumption (about a population parameter or distribution of the random variable)

Null Hypothesis (H0)
- no significant difference. It is what the researcher tries to disprove, reject, or nullify.

NOTE: You can think of null hypothesis as the CURRENT value you want to disprove in favor of alternative hypothesis.

Alternative Hypothesis (Ha)
- with real effect.

| Testing Hypothesis |

III. Level of Significance (α/alpha)

  • degree of significance in which we accept or reject the null.

  • 100% accuracy = not possible to accept/reject a hypothesis.

  • Significance level (α / alpha) - is the probability of making the wrong decision when the null is true.

  • Most common level of significance used = 1%, 5%, or 10%

IV. Two-tailed Test

  • when the alternative hypothesis is TWO sided.

V. One-tailed Test

  • hypothesis assumes a less than or greater than value.

| Graph Illustration |

VI. Rejection Region (also known as critical region)

  • is the set of all values of the test statistic that causes us to reject the null.

VII. Acceptance Region (non-rejection region)

  • is the set of all values or the test statistic that causes us to accept or fail to reject the null.

VIII. Critical Value

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  • is a point boundary on the test distribution that is compared to the test statistic to determine if the null would be rejected.

How to use T-Test?

| Errors |

IX. Type I Error

  • probability denoted by (α) alpha. Rejecting null when TRUE. (DOCTOR)

X. Type II Error

  • probability denoted by beta (β). Accepting null hypothesis when it is FALSE. (Nascam)

  • Alpha region - normal curve that shows the critical region.

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  • Beta region - normal curve that shows the acceptance region.