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.
ANOVA, which stands for Analysis of Variance, is a statistical technique used to compare the means of three or more samples to determine if at least one sample means significantly differs from the others. It is widely used in various fields such as business, medicine, and social sciences to test hypotheses on data sets.
A statistical method to test whether two (or more) categorical variables are independent or if they share a common proportion of observations. Frequently used in hypothesis testing and categorical data analysis.
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.
The F-statistic is a ratio of two variances used in various statistical tests including hypothesis testing for equal variances, equal means, and the relationship between dependent and independent variables.
A statistical procedure used to test the hypothesis that a particular probability distribution adequately fits an observed set of data.
Hypothesis testing is a statistical procedure that involves making a formal decision about whether a statement (hypothesis) about a population parameter should be accepted or rejected based on sample data.
Inferential statistics is the process of drawing information from sampled observations of a population and making conclusions about the population.
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.
A statistic is a descriptive measure calculated from data sampled from a population, used to make inferences about the overall population. It serves as a fundamental element in the field of statistics, aiding in data analysis, hypothesis testing, and predictive modeling.
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.
Trial and error is an empirical method used to test a hypothesis or procedure by repeating an experiment until the chance of error or desired outcome is reliably achieved. It is commonly used in situations where no established theory is available.
A two-tailed test, also known as a two-sided or nondirectional test, is a method in hypothesis testing that examines whether two estimates of parameters are equal without considering which one is smaller or larger. This type of test rejects the null hypothesis if the test statistic is significantly small or large.
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.
