Hypothesis Testing lecture

These flashcards cover key concepts of hypothesis testing, standard scores, and their applications in statistical analysis, as discussed in the lecture.

What is the purpose of using standard scores in data analysis?

To describe data points on different measures in terms of a common scale.

What are the most commonly used standard scores?

Z scores and T scores.

What is a z-score?

A z-score measures the distance between a data point and the mean of a variable, with a mean of 0 and a standard deviation of 1.

What is the formula for calculating a z-score?

z = (x - µ) / σ.

What is a T score?

A T score is a standardized score with a mean of 50 and a standard deviation of 10.

What is the formula for calculating a T score?

T = 10z + 50.

What is a percentile?

The proportion of the population scoring below a particular value.

What are the two main hypotheses in hypothesis testing?

Research hypothesis (alternative hypothesis) and null hypothesis.

What does rejecting the null hypothesis indicate?

It suggests that the proposed effect or research hypothesis is likely to be correct.

What is a Type I error?

An incorrect rejection of the null hypothesis.

What is a Type II error?

An incorrect failure to reject the null hypothesis.

What does statistical inference require?

Determining the likelihood that something could occur due to chance.

What does the term 'rejection region' refer to in hypothesis testing?

The range of values for which the null hypothesis can be rejected.

What does the area between a z-score and the mean represent?

The proportion of the population that falls within that range.

What are the implications of concluding that the alternative hypothesis is correct?

It indicates that the observed results are likely not due to chance.

What does it mean when Carl Sagan states 'Absence of evidence is not evidence of absence'?

Lack of evidence does not prove something does not exist.

In the context of hypothesis testing, what is a directional hypothesis?

A hypothesis predicting the direction of the expected effect.