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Unit 7 - Coggle Diagram
Unit 7
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Error
Type I Error
A Type I error, also known as a false positive, is an error that occurs when the null hypothesis is rejected when it should have been accepted.
as a lower alpha level (e.g. 0.01) will result in a higher probability of making a Type I error, while a higher alpha level (e.g. 0.1) will result in a lower probability of making a Type I error
Type II Error
A Type II error, also known as a false negative, is an error that occurs when the null hypothesis is not rejected when it should have been. This means that the null hypothesis is accepted when it is actually false.
A higher alpha level and a larger sample size will result in a lower probability of making a Type II error
KEY POINTS
Critical Value: A critical value is a specific value that is used to determine whether to reject or fail to reject the null hypothesis in a statistical test. It is compared with the test statistic to make this decision.
Random Sampling: Random sampling is a method of selecting a sample from a population in such a way that each individual has an equal chance of being chosen. It helps to ensure that the sample is representative of the entire population.
Alternative Hypothesis (Ha): The alternative hypothesis is a statement that contradicts or opposes the null hypothesis. It suggests that there is a significant difference or relationship between variables in a population.
Null Hypothesis (H0): The null hypothesis is a statement that assumes there is no significant difference or relationship between variables in a population. It represents the status quo or the default position.
Confidence Interval: A confidence interval provides an estimated range of values within which we believe the true population parameter lies. It quantifies the uncertainty associated with our estimate.
Two-Sample t-test: A two-sample t-test is a statistical test used to compare the means of two independent groups and determine if they are significantly different from each other.
T-test: A t-test is a statistical test used to compare two groups' means and determine if they are significantly different from each other. It assesses whether the difference between the means is due to chance or a real effect.
P-value: The p-value is the probability of obtaining an observed result (or more extreme) if the null hypothesis is true. It measures how strong the evidence against the null hypothesis is.
Inferential Procedures: Inferential procedures are statistical methods used to draw conclusions or make predictions about a population based on sample data. These procedures involve making inferences and generalizations from the sample to the larger population.
Significance Level: The significance level, also known as alpha (α), is the predetermined threshold used in hypothesis testing to determine whether there is enough evidence to reject the null hypothesis. It represents the probability of making a Type I error.
Hypothesis Test: A hypothesis test is a statistical procedure used to make inferences or draw conclusions about a population based on sample data. It involves formulating a null hypothesis and an alternative hypothesis, collecting and analyzing data, and making a decision regarding the hypotheses.
z-test: A z-test is a statistical test used to determine whether the means of two populations are significantly different when the population standard deviations are known.
Matched pairs t-test: A matched pairs t-test is a statistical test used to determine whether there is a significant difference between paired observations or measurements taken on the same subjects under different conditions.
Dependent samples t-test: A dependent samples t-test, also known as a paired-samples or repeated measures t-test, is used to determine whether there is a significant difference between the means of two related groups or conditions.