Question

I have a sample of 4 mice. I placod each mouse in a chamber with a lever and recorded how often the pressed the lever in a 2 miritn perod i heun spent a week tranng the mice using a schedule of reinforcement such that for every 30 levet presses they would receive a food pellot. At the ond of the week I placed eich moule in the same chamber asd recorded how otien they pressed the lever in 2 minuse petiod. The lever press data for each mouse is in the table below. Calculate the ditfecence score for each motse by subtracting the Pre-Training Counts from the Post-Training Cousts. Then help me determine if thore was a signicart charge in lever presses from pro-training to post-training. What is the standard error for yout test comparing pre- and post-training lever press trequency? Round finul answers ta swo decimal places. Do not round ary irtermedary values. Selected Answer: [None Given] Corfect Answer 2.89 Answer range +10(2.89−2.89)

Answer 1

To assess the change in lever presses, calculate the difference scores, perform a T-Test using the provided steps, which yields a significant result with a p-value of 0.0094 and a test statistic of -3.04.

Step-by-step explanation:

To calculate the difference score for each mouse, subtract the Pre-Training Counts from the Post-Training Counts. Once you have the list of differences, use a statistical software or calculator to perform a T-Test. The steps included pressing STAT, selecting TESTS, and choosing T-Test to input your data. For your test, you would input 0 as the hypothetical mean difference (MO), enter the list name where the data is stored, and set the frequency (Freq:) to 1. After that, choose the alternative hypothesis that the true mean difference is less than 0, then calculate or draw the test statistics. The provided p-value is 0.0094, and the test statistic is -3.04. These values indicate a significant change in lever presses from pre-training to post-training.

The standard error is not directly provided in the question, but typically can be calculated from the output of the T-Test, or by using the standard deviation of the difference scores divided by the square root of the sample size.