A null hypothesis, is a hypothesis that states that there is no statistical significance between the two variables in the hypothesis. It is the hypothesis that the researcher is trying to disprove. The symbol H₀ is used to represent null hypothesis.
An alternative hypothesis states that there is statistical significance between two variables. The alternative hypothesis is the hypothesis that the researcher is trying to prove.
![H_(\alpha)\text{ is the symbol used to represent alternative hypothesis}](https://img.qammunity.org/2023/formulas/mathematics/college/emv9pdsr89dxlmpc8kok1ml1bu655bhr6b.png)
Examples:
Null Hypothesis: The world is not round.
Alternate Hypothesis: The world is round
Type 1 error is the error caused by rejecting a null hypothesis when it is true.
Type II error is the error that occurs when the null hypothesis is accepted when it is not true.
P-value is used in hypothesis testing to help you support or reject the null hypothesis. The p value is the evidence against a null hypothesis. The smaller the p-value, the stronger the evidence that you should reject the null hypothesis. If the p-value is less than or equal to alpha we reject the null hypothesis otherwise we fail to reject.
A Student t-distribution is a statistical test that compares the means of two samples. It is used in hypothesis testing, with a null hypothesis that the difference in group means is zero and an alternate hypothesis that the difference in group means is different from zero.t-test is used when sample size is small (n<50) and population variance is unknown is used when sample size is small (n<50) and population variance is unknown