Hypothesis Testing: What's It All About?
Are you curious about the power of hypothesis testing? Discover how you can use hypothesis testing to draw meaningful conclusions.
What is Hypothesis Testing?
Hypothesis testing is a statistical technique used to test a claim or assumption about a population parameter.
What is a null hypothesis?
The null hypothesis is a statement that states that there is no significant difference between the population parameter and the sample statistic. The null is assumed to be true until proven otherwise.
What is a test statistic?
A Test statistic is a numerical value calculated from a sample and determines the probability that the results of a sample are due to chance, or if there is a statistically significant difference between the sample and the population. It used to draw conclusions about the population based on the sample data.
What is a significance level?
A significance level is a predetermined probability used in hypothesis testing to determine whether a test statistic is sufficiently far from the population's mean or expected value. The significance level is typically set to 0.05, which means that if the test statistic falls within the area of rejection, then the null hypothesis can be rejected and the alternative hypothesis accepted.
How do we make decisions?
If the test statistic falls within the rejection area, the null hypothesis can be rejected, and the alternative hypothesis can be accepted. If the test statistic falls outside the rejection area, then the null hypothesis cannot be rejected.
Hypothesis testing is a powerful tool used by to draw conclusions about a population based on sample data. By taking a sample and calculating a test statistic, we can determine whether the null hypothesis can be rejected. If the null hypothesis is rejected then the alternative hypothesis can be accepted. However, this does not prove the alternative hypothesis is true. It simply means that the data provide enough evidence to suggest that the alternative hypothesis may be true.
