Class 8 Exam > Class 8 Notes > BBC Compacta Solution For Class 8 > Practice Assignment: 30
Practice Assignment: 30 | BBC Compacta Solution For Class 8 PDF Download
TYPE-I
Fill in the blanks with the appropriate option from those in brackets
(1)
Ans (a) causes
Ans (b) when
Ans (c) the
Ans (d) with
Ans (e) after
(2)
Ans (a) that
Ans (b) From
Ans (c) all
Ans (d) was given
Ans (e) the
Ans (f) he
(3)
Ans (a) are returning
Ans (b) these
Ans (c) has
Ans (d) with
Ans (e) seeng
Ans (f) though
(4)
Ans (a) biggest
Ans (b) by
Ans (c) emits
Ans (d) to
(5)
Ans (a) made
Ans (b) when
Ans (c) will
Ans (d) better
(6)
Ans (a) Have
Ans (b) why
Ans (c) because
Ans (d) with
Ans (e) cannot
Ans (f) a
TYPE-II
Complete the following passages by filling in the blanks with suitable words. Write your answers in the space provided.
(7)
Ans (a) the
Ans (b) to
Ans (c) her
Ans (d) other
Ans (e) in
Ans (f) with
(8)
Ans (a) by
Ans (b) than
Ans (c) on
Ans (d) are
Ans (e) from
Ans (f) to
(9)
Ans (a) of
Ans (b) of
Ans (c) an
Ans (d) are
Ans (e) many
Ans (f) in
(10)
Ans (a) from
Ans (b) his
Ans (c) has
Ans (d) them
Ans (e) to
Ans (f) of
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FAQs on Practice Assignment: 30 - BBC Compacta Solution For Class 8
| 1. What is the importance of understanding the difference between Type-I and Type-II errors in statistics? |  |
Ans.Understanding the difference between Type-I and Type-II errors is crucial in statistics because it helps researchers and analysts assess the reliability of their hypothesis tests. A Type-I error occurs when a true null hypothesis is incorrectly rejected, leading to a false positive conclusion. Conversely, a Type-II error happens when a false null hypothesis is not rejected, resulting in a missed opportunity to identify a significant effect. Recognizing these errors aids in designing better experiments and interpreting results more accurately.
| 2. How can one minimize Type-I and Type-II errors in hypothesis testing? | |
Ans.To minimize Type-I errors, researchers can lower the significance level (alpha) chosen for the test. This reduces the likelihood of falsely rejecting the null hypothesis. To minimize Type-II errors, increasing the sample size can enhance the test's power, making it more likely to detect an effect when one exists. Additionally, using more precise measurement tools and techniques can also contribute to reducing both types of errors.
| 3. What are some real-world examples of Type-I and Type-II errors? | |
Ans.A real-world example of a Type-I error is a medical test that indicates a patient has a disease when they are actually healthy, leading to unnecessary treatment. An example of a Type-II error is a cancer screening test that fails to detect cancer in a patient who actually has it, resulting in a lack of necessary treatment. These examples illustrate the significant consequences of each type of error in decision-making processes.
| 4. How do Type-I and Type-II errors relate to the significance level in hypothesis testing? | |
Ans.Type-I and Type-II errors are directly influenced by the significance level (alpha) set for a hypothesis test. A lower significance level decreases the probability of a Type-I error but may increase the chance of a Type-II error, as it becomes harder to reject the null hypothesis. Conversely, raising the significance level reduces the chance of a Type-II error but increases the risk of a Type-I error. Finding the right balance is essential for effective hypothesis testing.
| 5. What statistical methods can help in understanding and mitigating Type-I and Type-II errors? | |
Ans.Statistics provides several methods to understand and mitigate Type-I and Type-II errors. Techniques such as power analysis can help determine the appropriate sample size needed to achieve a desired level of confidence while minimizing errors. Additionally, using confidence intervals allows researchers to gauge the range of values that are plausible for the true parameter, thus providing more context around the potential for Type-I and Type-II errors.
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Oct 09, 2025
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