Hypothesis Testing
Data Science is a growing field that has seen a boost in popularity in recent years. An important component of data science is the use of Hypothesis testing, which can be used to make conclusions from data and make informed decisions.
What is Hypothesis Testing?
Hypothesis testing is a common method used in data science to evaluate the validity of a hypothesis about a population parameter. It is used to determine whether the data support the hypothesis, or if there is enough evidence to refuse it.
Important parameters of Hypothesis Testing
1: Null Hypothesis (H0): The null hypothesis, denoted by H0, represents the statement that there is no relationship between the variables being studied.
By default, the Null Hypothesis will always be True.
For e.g. A company produces 50 units per day.
2: Alternative Hypothesis (H1): The alternative hypothesis, denoted by H1, is the statement opposite to the Null Hypothesis.
For e.g. A company does not produce 50 units per day.
3: Level of Significance: The Level of significance is the probability limit or threshold used to accept or reject the Null Hypothesis. The level of Significance is usually selected as 5%.
4: P-Value: The P-value is the probability of obtaining the observed results when the Null Hypothesis is True. We reject the Null Hypothesis if the p-value is less than the level of significance.
Formulating the Hypothesis and Calculating Test Statistics
To conduct a hypothesis test, we first formulate a null hypothesis and an alternative hypothesis. After this, we collect a sample of data from the population and use it to calculate a test statistic.
Interpreting the Results
Based on the calculated test statistic, we then compute the p-value. If the p-value is less than the level of significance(usually 5%) we reject the Null Hypothesis otherwise we fail to reject the Null hypothesis or accept Null Hypothesis.
Errors in Hypothesis Testing
1: Type I Error: A type I error occurs when we reject the Null Hypothesis but it is actually True.
2: Type II Error: Type II error occurs when we accept the null hypothesis but it is False. It is also known as a false negative.
Types of Hypothesis Tests
There are many different types of hypothesis tests, including t-tests, chi-square tests, and F-tests, each of which is used to test different types of hypotheses. For e.g. chi-square is used to test categorical variables while a t-test is used to find the difference in means of two categories.
Conclusion
In conclusion, hypothesis testing is a critical tool in data science for evaluating the validity of hypothesis about population parameters. By carefully designing experiments and interpreting the results, we can use hypothesis testing to draw conclusions and make decisions based on data.
