In statistical testing, an alternative hypothesis is accepted if a sample contains sufficient evidence to reject the null hypothesis. It is usually denoted by H₁. In most cases, the alternative hypothesis is the expected conclusion, which is why the test was conducted in the first place.
In statistical hypothesis testing, the critical region refers to the set of values for the test statistic that leads to the rejection of the null hypothesis.
In statistical hypothesis testing, the null hypothesis (H0) is the default or initial statement assumed to be true, often stating that there is no effect or no difference. The null hypothesis is only rejected if the evidence from the data significantly contradicts it.
A p-value is a statistical measure that helps researchers determine the significance of their results. This value helps assess whether the observed data supports the null hypothesis or not.
Statistically significant is a term used in hypothesis testing to determine whether a test statistic meets or exceeds a predetermined threshold, leading to the rejection of the null hypothesis.
The T-statistic is a statistical measure used to compare the means of two groups or to assess if a sample mean significantly differs from a known value. It is instrumental in hypothesis testing, particularly when the sample size is small, and the population standard deviation is unknown.
A measure calculated from data sampled from a population, used to either reject or fail to reject the null hypothesis.
In statistical testing, a Type 2 Error occurs when the null hypothesis is not rejected even though it is false. This error, also known as a false negative, has significant implications in hypothesis testing as it can lead to incorrect conclusions.
In statistical hypothesis testing, a Type I Error occurs when the null hypothesis is rejected when it is actually true. This incorrect rejection leads to a false positive result.
