A two-sample t-test is a statistical test used to compare the means of two different samples to determine if there is a significant difference between them. It is based on the assumption that the samples are drawn from populations with normal distri...

 A one-proportion z-test is a statistical test used to compare the proportion of a sample to a known population proportion. It is used to test a hypothesis about the population proportion and is based on the assumption that the sample is drawn from a...

 A one-sample t-test is a statistical test used to compare the mean of a sample to a known population mean. It is used to test a hypothesis about the population mean and is based on the assumption that the sample is drawn from a normally distributed...

The p-value is the probability of obtaining a test statistic that is equal to or more extreme than the one observed, assuming that the null hypothesis is true. It is used in hypothesis testing to determine the significance of the observed results. Fo...

 A two-sample z-test is a statistical test used to compare the means of two different samples to determine if there is a significant difference between them. It is based on the assumption that both samples are drawn from normally distributed populati...

 A one-sample z-test is a statistical test used to compare the mean of a sample to a known population mean. It is used to test a hypothesis about the population mean and is based on the assumption that the sample is drawn from a normally distributed...

Hypothesis testing is a fundamental tool in statistical analysis that allows us to make decisions about a population based on sample data. It involves formulating a hypothesis about a population parameter, collecting data, and using statistical techn...

The statistical power of the test is the probability of correctly rejecting the null hypothesis when it is false. In other words, it is the probability of not making a type II error in a hypothesis test. The relationship between the power of the test...

Confidence level and significance level are two important concepts in statistical hypothesis testing. The confidence level measures how confident we are that our conclusions are correct. In contrast, the significance level (also called alpha value) i...

In hypothesis testing, two types of errors can occur: type I and type II. These errors refer to the incorrect rejection or acceptance of the null hypothesis respectively.Type I Error (Alpha)A type I error occurs when the null hypothesis is true but i...