Dell

Hypothesis Testing is a process by which an analyst tests a statistical hypothesis. The methodology employed by the analyst depends on the nature of the data used, and the goals of the analysis. The goal is to either accept or reject the null hypothesis.

A one-sample test, also known as a goodness of fit test, shows whether the collected data is useful in making a prediction about the population.

1. Create a significance level: 1%, 5%, or 10% (probability of committing a type 1 error)

2. State the hypotheses

3. Select Statistical Inference in Stat Tools, then Hypothesis Test, then Mean Standard/ Deviation

4. Reject the P-value is less than the significance level.

Two-sample two sided test un-stacked data: A two-sample test, also called a test of independence, is used to determine if one variable is dependent on one another.

1. Create a significance level: 1%, 5%, or 10% (probability of committing a type 1 error)

2. State the hypotheses

3. Select Statistical Inference in Stat Tools, then Hypothesis Test, then Mean Standard/ Deviation

4. Reject the P-value is less than the significance level.

Two-sample two sided stacked data:

1. Create a significance level: 1%, 5%, or 10% (probability of committing a type 1 error)

2. State the hypotheses

3. Select Statistical Inference in Stat Tools, then Hypothesis Test, then Mean Standard/ Deviation

4. Reject the P-value is less than the significance level.

Covariance and correlation are very similar; both describe the degree of similarity between two random variables or sets of random variables.

P-value is the probability of obtaining a test statistic at least as extreme as the one that was actually observed, assuming that the null hypothesis is true. One often "rejects the null hypothesis" when the p-value is less than the significance level α (Greek alpha), which is often 0.05 or 0.01. When the null hypothesis is rejected, the result is said to be statistically significant....