__What is Hypothesis Testing?__

__What is Hypothesis Testing?__

- Hypothesis testing is a statistical test used to determine the relationship between two data sets, between two or more independent and dependent variables.
- Hypothesis testing is a scientific process to conclude whether to reject or not reject the null hypothesis.

**Hypothesis testing consists of four steps:**

**1. State hypothesis:**

a) State the null hypothesis

b) State an alternative hypothesis

**2. Formulate an analysis plan including determination of significance level**

**3. Analyze sample data and calculate the p-value, Odds Ratio and Confidence Interval**

**4. Interpret results**

__Importance of Hypothesis Testing:__

__Importance of Hypothesis Testing:__

- A hypothesis test evaluates two mutually exclusive statement about a population to determine which statement is supported by the sample data.
- According to the San Jose State University Statistics Department:
- Hypothesis testing is one of the most important concepts in statistics because it is how we decide if something really happened, or if certain treatments have positive effects, or if groups differ from each other or if one variable predicts another.

__What do you Mean by Errors in Hypothesis?__

__What do you Mean by Errors in Hypothesis?__

In the process of testing hypotheses, there are two major types of error. They are:

- Type I error
- Type II error

__Differences Between Type I and Type II Error:__

__Differences Between Type I and Type II Error:__

Basis of Difference |
Type I Error |
Type II Error |

Occurrence |
A type I error occurs when the null hypothesis is true but is rejected. | A type II error occurs when the null hypothesis is false but invalidly fails to be rejected. |

Comparison |
A type I error is False positive. |
A type II error is False negative |

Designation |
The probability that we will make a type I error is designated ‘α’ (alpha). | Probability that we will make a type II error is designated ‘β’ (beta). |

Probability of committing error |
Equals to the level of significance (α)‘α’ is the so-called p-value. |
Equals to the power of a test.The probability 1- ‘β’ is called the power of the study. |

Represents |
A false hit. | A miss. |

Nature |
We may reject the null hypothesis when the null hypothesis is true is known as Type I error. | We may accept the null hypothesis, when in fact null hypothesis is not true is known as Type II error. |

Importance |
Type I errors are generally considered more serious. | Type II errors are given less preference. |

Acceptance |
It refers to non-acceptance of hypothesis, which ought to be accepted. | It refers to the acceptance of hypothesis, which ought to be rejected. |

Consequence |
The probability of Type I error reduces with lower values of (α) since the lower value makes it difficult to reject null hypothesis. |
The probability of Type II error reduces with higher values of (α) since the higher value makes it easier to reject the null hypothesis. |

**Public Health Notes**