In lecture, we noted that experiments test the Null hypothesis . One of the reasons for this is that they have greater precision .

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In this item, the blanks to be filled are drawn from the idea that experiments test a specific core statement and that there is a methodological consequence related to precision.
Option 1: 'Null hypothesis' — The claim that experiments test the null hypothesis is consistent with standard research practice. In experimental design, researchers typically set up hypotheses that include a null component (no effect or no difference) and an alternative. The analysis then assesses whether the observed data provide enough evidence to reject that null assumption at a chosen significance level. This framing is central because it creates a clear, falsifiable statement that allows for probabilistic inference.
Option 2: 'precision' — The second blank being filled with 'precision' suggests that experiments offer greater precision as a reason for testing the null hypothesis. While improved precision is often a desirable feature of well-controlled experiments (through randomization, control of confounding variables, repeated measurements, and standardized procedures), it’s more accurate to say that precision enhances our ability to detect deviations from the null and to estimate effects with smaller standard errors. In other words, increased precision improves the reliability of hypothesis tests, enabling more confident conclusions about whether to reject the null. This makes the claim reasonable in the sense that precision underpins the strength of the test, even though the primary conceptual justification for testing the null is to provide a probabilistic framework for inference.
Potential misconceptions to watch for: some might think experiments test any claim directly, but the standard framework is to test a null hypothesis against an alternative, which provides a rigorous criterion for evidence. Others may underestimate the role of precision, but without precise estimation and low measurement error, tests may lack power to detect true effects. By recognizing both blanks as given, we acknowledge the typical structure of hypothesis testing and the role of experimental precision in enabling reliable conclusions.

COGS14A_SP25_A00 [COGS 14A, SP25] Quiz 3, due before the midterm on 4/21/25

In lecture, we noted that experiments test the Null hypothesis . One of the reasons for this is that they have greater precision .

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COGS14A_SP25_A00 [COGS 14A, SP25] Quiz 3, due before the midterm on 4/21/25

In lecture, we noted that experiments test the Null hypothesis . One of the reasons for this is that they have greater precision .

View Explanation

Log in for full answers

Similar Questions

Which one is true?

For an upper-tail hypotheses about a population mean with σ known and α=5%, which one is the rejection region?

For a given sample, the P-value for upper tail test is x, and the P-value for two-tailed test is y, then

For a two-tailed hypothesis test with a test statistic value of z = 2.09, the p-value is _____.

For a sample size of 30, changing from using the t distribution to using the standard normal distribution in a hypothesis test _____.

Exhibit 9-5 n = 16 H0: μ ≥ 80 = 75.607 Ha: μ < 80 σ = 8.6 Assume the population is normally distributed. Refer to Exhibit 9-5. The p-value is equal to _____.

Exhibit 9-1 ​ ​ Refer to Exhibit 9-1. If the test is done at a .01 level of significance, the null hypothesis should _____.

What conclusion for a test would correctly detect a difference between the actual parameter and the hypothesized value for the parameter? c08.p089.q0008

More Practical Tools for Students Powered by AI Study Helper

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Exhibit 9-1 Refer to Exhibit 9-1. If the test is done at a .01 level of significance, the null hypothesis should _____.