Measure Up

Measurement is the process of determining the value or quantity of something. An example of measurement in daily life is measuring the time it takes to cook a meal.

The types of errors in measurement include gross errors, systematic errors, and random errors.

Gross errors in measurement are mistakes or inaccuracies that lead to significantly incorrect measurements. They can be caused by human error, equipment malfunction, or environmental factors.

<p>Systematic errors in measurement are consistent errors that occur due to a flaw or bias in the measurement process. They can result in measurements that are consistently too high or too low.</p> Signup and view all the answers

<p>Random errors in measurement are unpredictable fluctuations or variations in measurement readings. They can be caused by factors such as noise, fluctuations in environmental conditions, or limitations of the measuring instrument.</p> Signup and view all the answers

<p>Null hypothesis = Assumes no relationship or no difference between groups Parametric tests = Assume a fixed set of parameters to determine a probabilistic model Statistical tests = Used to check whether the null hypothesis is rejected or not rejected Non-parametric tests = Do not make any assumptions about the probability distribution of the population</p> Signup and view all the answers

<p>Statisticians = Heavily use hypothesis testing in their work Machine Learning Engineers = Use hypothesis testing to evaluate the performance of their models Data Scientists = Use hypothesis testing to make data-driven decisions Web Developers = Do not typically use hypothesis testing in their work</p> Signup and view all the answers

<p>Type I error = Rejecting the null hypothesis when it is true Type II error = Failing to reject the null hypothesis when it is false Power of a test = Probability of correctly rejecting the null hypothesis Significance level = Probability of making a Type I error</p> Signup and view all the answers

<p>One-tailed test = Tests a specific direction of effect Two-tailed test = Tests for any direction of effect Critical value = Value used to determine if the null hypothesis should be rejected P-value = Probability of observing a test statistic as extreme as the one calculated, assuming the null hypothesis is true</p> Signup and view all the answers

<p>Type I error = Occurs when we reject the null hypothesis even though it is true Type II error = Occurs when we fail to reject the null hypothesis even though it is false Correct decision = Occurs when we make the right decision based on the hypothesis test Statistical error = Any error that occurs during the process of hypothesis testing</p> Signup and view all the answers