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Chapter 3: Problem 38

True or false, Part. II. Determine if the statements below are true or false. For each false statement, suggest an alternative wording to make it a true statement. (a) As the degrees of freedom increases, the mean of the chi-square distribution increases. (b) If you found \(X^{2}=10\) with \(d f=5\) you would fail to reject \(H_{0}\) at the \(5 \%\) significance level. (c) When finding the p-value of a chi-square test, we always shade the tail areas in both tails. (d) As the degrees of freedom increases, the variability of the chi-square distribution decreases.

Short Answer

Expert verified
(a) True; (b) True; (c) False, shade the right tail; (d) False, variance increases with degrees of freedom.

Step by step solution

01

Analyze Statement (a)

The mean of a chi-square distribution is equal to its degrees of freedom. As the degrees of freedom increase, the mean of the chi-square distribution also increases. Therefore, Statement (a) is True.
02

Analyze Statement (b)

First, calculate the critical value for chi-square with degrees of freedom (df) = 5 at a 5% significance level. This is approximately 11.07 (using statistical tables or software). Since our calculated value of \(X^2 = 10\) is less than 11.07, we fail to reject \(H_0\). Thus, Statement (b) is True.
03

Analyze Statement (c)

Chi-square tests are generally one-tailed because they measure the discrepancy between observed and expected data, usually resulting in positive values on the left tail. There is typically no need to shade both tails. Thus, Statement (c) is False. A true statement would be: 'When finding the p-value of a chi-square test, we only shade the right tail.'
04

Analyze Statement (d)

The variability of a chi-square distribution is characterized by its variance, which is equal to twice its degrees of freedom. Therefore, as the degrees of freedom increase, the variability (variance) also increases. Statement (d) is False. A true statement would be: 'As the degrees of freedom increase, the variability of the chi-square distribution increases.'

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Key Concepts

These are the key concepts you need to understand to accurately answer the question.

Degrees of Freedom
Degrees of freedom are a crucial concept in statistics, particularly when it involves distributions like the chi-square distribution. Think of them as the number of independent pieces of information you have to estimate another piece of data. In essence, they help determine the shape of the chi-square distribution. As the degrees of freedom increase, the distribution changes in two main ways:
  • The mean of the distribution increases. Specifically, the mean of a chi-square distribution is equal to its degrees of freedom. So, if you have 5 degrees of freedom, the mean would be 5.
  • The shape of the distribution becomes more symmetrical. With fewer degrees of freedom, the chi-square distribution is skewed to the right, but as they increase, the distribution morphs to look more like a normal distribution.
Increasing the degrees of freedom generally indicates more data points or categories, which allows for a more refined and accurate analysis. Understanding this concept will help you in interpreting results from chi-square tests accurately.
P-Value
The p-value is a fundamental concept in hypothesis testing, providing a measure of the strength of the evidence against the null hypothesis. When you conduct a chi-square test, the p-value helps determine whether to reject or fail to reject the null hypothesis. Here's what you need to know:
  • A smaller p-value (usually less than 0.05) suggests strong evidence against the null hypothesis, leading you to reject it in favor of the alternative hypothesis.
  • If the p-value is larger than your significance level (commonly 0.05), you fail to reject the null hypothesis, indicating the observed data is not significantly different from what the null hypothesis predicts.
In chi-square tests, p-values are derived by comparing your test statistic to a chi-square distribution with a specific degrees of freedom. This comparison determines the probability of observing a statistic as extreme as, or more extreme than, the test statistic under the null hypothesis. Understanding p-values in this context will enhance your interpretation of statistical tests.
Statistical Significance
Statistical significance is a critical aspect of conducting hypothesis tests, such as the chi-square test. It tells us whether an observed effect or relationship in the data is likely due to something other than random chance.
  • To determine if a result is statistically significant, compare the p-value to a predetermined significance level (usually 0.05). If the p-value is lower, the effect is deemed statistically significant.
  • When a result is statistically significant, it suggests that there's a small likelihood the observed data would occur under the null hypothesis.
Statistical significance doesn鈥檛 necessarily imply practical significance. It means that the result is unlikely due to chance given the null hypothesis is true. This concept is vital in making decisions based on statistical data, as it guides whether to reject or stick with the null hypothesis.
Variance in Statistics
Variance is a measure of how spread out numbers in a data set are, and it's vital for understanding the variability within a statistical distribution. In the context of the chi-square distribution, variance is particularly noteworthy.
  • For a chi-square distribution, the variance is equal to twice the degrees of freedom. This means that as the degrees of freedom increase, so does the variability of the distribution.
  • A greater variance signifies that the data points in the distribution are more spread out from the mean, indicating a higher level of variability.
Variance in statistics is essential as it helps describe the uncertainty and the dispersion of data, influencing how well we can predict outcomes from the data. Understanding variance helps you understand how precise your statistical estimations are in relation to the chi-square distribution.

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Most popular questions from this chapter

The Daily Show. A 2010 Pew Research foundation poll indicates that among 1,099 college graduates, \(33 \%\) watch The Daily Show. Meanwhile, \(22 \%\) of the 1,110 people with a high school degree but no college degree in the poll watch The Daily Show. A \(95 \%\) confidence interval for ( pcollege grad - Pus ar leas), where \(p\) is the proportion of those who watch The Daily Show, is (0.07,0.15) . Based on this information, determine if the following statements are true or false, and explain your reasoning if you identify the statement as false. (a) At the \(5 \%\) significance level, the data provide convincing evidence of a difference between the proportions of college graduates and those with a high school degree or less who watch The Daily Show. (b) We are \(95 \%\) confident that \(7 \%\) less to \(15 \%\) more college graduates watch The Daily Show than those with a high school degree or less. (c) \(95 \%\) of random samples of 1,099 college graduates and 1,110 people with a high school degree or less will yield differences in sample proportions between \(7 \%\) and \(15 \%\). (d) A \(90 \%\) confidence interval for ( \(p\) college grad \(-\) pus ar lew ) would be wider. (e) A \(95 \%\) confidence interval for (pus ax less \(-p_{\text {college grad }}\) ) is (-0.15,-0.07) .

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