null measurements

(noun)

methods of measuring current and voltage more accurately by balancing the circuit so that no current flows through the measurement device

Related Terms

Examples of null measurements in the following topics:

  • Null Measurements

    • Null measurements balance voltages so there is no current flowing through the measuring devices that would interfere with the measurement.
    • Null measurements balance voltages, so there is no current flowing through the measuring device and the circuit is unaltered.
    • A potentiometer is a null measurement device for measuring potentials (voltages).
    • A variety of bridge devicesare used to make null measurements in circuits .
    • The potentiometer is a null measurement device.

  • Kelvin Scale

    • The kelvin is a unit of measurement for temperature; the null point of the Kelvin scale is absolute zero, the lowest possible temperature.
    • The kelvin is a unit of measurement for temperature.
    • The Kelvin scale is an absolute, thermodynamic temperature scale using absolute zero as its null point.
    • The choice of absolute zero as null point for the Kelvin scale is logical.
    • The kelvin is the primary unit of measurement in the physical sciences, but it is often used in conjunction with the degree Celsius, which has the same magnitude.

  • The Null and the Alternative

    • The alternative hypothesis and the null hypothesis are the two rival hypotheses that are compared by a statistical hypothesis test.
    • The null hypothesis refers to a general or default position: that there is no relationship between two measured phenomena, or that a potential medical treatment has no effect.
    • Rejecting or disproving the null hypothesis (and thus concluding that there are grounds for believing that there is a relationship between two phenomena or that a potential treatment has a measurable effect) is a central task in the modern practice of science and gives a precise sense in which a claim is capable of being proven false.
    • Since the null and alternate hypotheses are contradictory, we must examine evidence to decide if there is enough evidence to reject the null hypothesis or not.
    • Sir Ronald Fisher, pictured here, was the first to coin the term null hypothesis.

  • The t-Test

    • A t-test is any statistical hypothesis test in which the test statistic follows a Student's t-distribution if the null hypothesis is supported.
    • A two-sample location test of a null hypothesis that the means of two normally distributed populations are equal.
    • A test of a null hypothesis that the difference between two responses measured on the same statistical unit has a mean value of zero.
    • For example, suppose we measure the size of a cancer patient's tumor before and after a treatment.
    • This is often referred to as the "paired" or "repeated measures" t-test.

  • Power and the Type 2 Error rate

    • What is the probability of successfully rejecting the null hypothesis?
    • Step 1: The null distribution could be represented by N(130,2.5), the same standard deviation as the true distribution but with the null value as its center.
    • The probability of rejecting the null hypothesis is called the power.
    • However, when the truth is far from the null value, where we use the standard error as a measure of what is far, the power tends to increase.
    • When the truth is far from the null value, the point estimate also tends to be far from the null value, making it easier to detect the difference and reject H 0 .

  • Introduction

    • The two groups could not have exactly the same mean age (if measured precisely enough such as in days).
    • This null hypothesis can be written as:
    • Although the null hypothesis is usually that the value of a parameter is 0, there are occasions in which the null hypothesis is a value other than 0.
    • If the null hypothesis is rejected, then the alternative to the null hypothesis (called the alternative hypothesis) is accepted.
    • If the null hypothesis

  • Significance Levels

    • If a test of significance gives a $p$-value lower than or equal to the significance level, the null hypothesis is rejected at that level.
    • The key preparatory computation is computing the cumulative distribution function (CDF) of the sampling distribution of the test statistic under the null hypothesis, which may depend on parameters in the null distribution and the number of samples in the data.
    • If a test of significance gives a $p$-value lower than or equal to the significance level, the null hypothesis is rejected at that level.
    • The vertical coordinate is the probability density of each outcome, computed under the null hypothesis.
    • Outline the process for calculating a $p$-value and recognize its role in measuring the significance of a hypothesis test.

  • Goodness of Fit

    • A measure of goodness of fit typically summarize the discrepancy between observed values and the values expected under the model in question.
    • Such measures can be used in statistical hypothesis testing, e.g., to test for normality of residuals or to test whether two samples are drawn from identical distributions.
    • The null hypothesis for the above experiment is that the observed values are close to the predicted values.
    • Thus in this case the null and alternative hypotheses corresponds to:
    • Therefore the null hypothesis is not rejected, and the coin toss was fair.

  • Significance Testing and Confidence Intervals

    • Explain why a confidence interval makes clear that one should not accept the null hypothesis
    • Since zero is lower than 2.00, it is rejected as a plausible value and a test of the null hypothesis that there is no difference between means is significant.
    • Therefore, even before an experiment comparing their effectiveness is conducted, the researcher knows that the null hypothesis of exactly no difference is false.
    • If the 95% confidence interval contains zero (more precisely, the parameter value specified in the null hypothesis), then the effect will not be significant at the 0.05 level.
    • However, there is an infinite number of other values in the interval (assuming continuous measurement), and none of them can be rejected either.

  • Choosing a significance level

    • Here we want to be cautious about failing to reject H 0 when the null is actually false.
    • The null hypothesis is that the more expensive parts last no more than 12% longer while the alternative is that they do last more than 12% longer.
    • The null hypothesis would be that the suppliers' parts are equally reliable.
    • However, the machine usually functions properly even if this part is broken, so the part is replaced only if we are extremely certain it is broken based on a series of measurements.
    • 4.40: Here the null hypothesis is that the part is not broken, and the alternative is that it is broken.