Question

Given the description of a set of crosses, be able to determine whether the expected ratios are consistent with what is really observed using the Chi-Square test.

A. UNDER 0.05 you REJECT null hypothesis
B. OVER 0.05 you ACCEPT null hypothesis

C. Low Chi square indicate high probablility of the observed variation COULD be due to random chance alone

D. High Chi square values indicate a low probablility that the observed deviations ARE due to random chance alone

E. Normal level of significance is 5%

Answer 1

The Chi-Square test is used to determine if expected ratios are consistent with observed results. A low Chi-Square value suggests random chance alone explains variations, while a high Chi-Square value suggests other factors are at play. The test is accepted or rejected based on the level of significance.

Step-by-step explanation:

The Chi-Square test is used to determine whether the expected ratios in a set of crosses are consistent with the observed results. In this test, the null hypothesis is rejected if the calculated Chi-Square value is below 0.05, indicating that the observed variations are not due to random chance alone. Conversely, if the calculated Chi-Square value is above 0.05, the null hypothesis is accepted. A low Chi-Square value indicates a high probability that the observed variations could be due to random chance alone, while a high Chi-Square value suggests a low probability that the observed deviations are due to random chance alone. The normal level of significance for this test is 5%.