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  • Dashboard
Topics
Exponents & LogarithmsRounding & ErrorSequences & SeriesFinancial MathematicsMatricesComplex Numbers
Cartesian plane & linesFunction TheoryModellingTransformations & asymptotes
2D & 3D GeometryVoronoi DiagramsTrig equations & identitiesVectorsGraph Theory
ProbabilityDescriptive StatisticsBivariate StatisticsDistributions & Random VariablesInference & Hypotheses
DifferentiationIntegrationDifferential Equations
Paper 3
Plus
Calculator Skills
Review VideosFormula BookletAll Study Sets
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Perplex
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Inference & Hypotheses
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Z-test and Confidence Intervals
Further χ² tests & unbiased estimators
Z-test and Confidence Intervals
Inference & Hypotheses

Z-test and Confidence Intervals

0 of 0 exercises completed

Z-tests and confidence intervals for a population mean using technology, including Z-Test for one sample, two-sample and paired samples via differences with ​μ0​=0, normal confidence intervals when ​σ​ is known, t-intervals when ​σ​ is unknown, and the use of critical values, critical regions and confidence levels.

Want a deeper conceptual understanding? Try our interactive lesson!

Understanding confidence intervals
AHL AI 4.16

A confidence interval is a range of values in which we believe the true mean of the population lies.

When we take samples from a larger population, our samples tend to cluster around the mean of the population. Using a sample, we can make an educated guess that the true population mean is in some range. The more data we have, the better our guess gets.

We calculate this interval in line with the level of certainty we want to have that our range contains the true mean. Obviously, we can be ​100%​ sure that the value is between ​−∞​ and ​+∞, but the smaller the range, the lower the confidence level we can have.

Normal confidence interval using technology
AHL AI 4.16

If we know the standard deviation of the population whose mean ​μ​ we want a confidence interval for, we use a so called normal confidence interval.


Your calculator should include a statistical test called Zinterval or similar. To use it:

  1. Enter the value of ​σ, which must be known for a Z-test of any kind.

  2. Enter either

    • Data: a list of values you've typed into the calculator

    • Stats: the sample mean ​xˉ​ and ​n, the number of samples.

  3. Enter the confidence level and hit calculate

The calculator returns the desired interval, which is symmetrical around ​xˉ.

T-interval confidence interval using technology
AHL AI 4.16

To find a confidence interval for the mean without knowing the variance, we first have to use our sample to first estimate the standard deviation. Because of this extra uncertainty, we switch to using a t-distribution.


Your calculator should include a statistical test called Tinterval or similar. To use it:

  1. Enter either

    • Data: a list of values you've typed into the calculator

    • Stats: the sample mean ​xˉ, the sample standard deviation ​Sx​ and ​n, the number of samples.

  2. Enter the confidence level and hit calculate

The calculator returns the desired interval, which is symmetrical around ​xˉ.

Z-Test for population mean
AHL AI 4.18

Z-tests allow us to test the mean of a sample against

  • a population with known mean: use Z-Test

  • another sample: use 2-SampZTest

  • a paired sample: calculate the difference, then use Z-Test with ​μ0​=0.

When testing for the mean, or finding confidence intervals, we have to decide between using a T-test or a Z-test:

  • We use ​Z​ (normal) when the standard deviation is known.

  • We use ​T​ when the standard deviation is not known.

The ​T​-distribution is very close to the normal, except that is slightly wider to account for the fact that we are estimating the standard deviation using a sample.

Critical values & regions
AHL AI 4.18

When testing the mean of a sample against a population, the critical region is the set of values for the sample mean that would lead to rejecting the null hypothesis. The critical value(s) is (are) the boundary of the critical region. In other words, the critical value is the threshold for ​xˉ​ that leads to a ​p​ value exactly equal to the chosen significance level.

​μ<μ0​​

​μ=μ0​​

​μ0​<μ​

​c=invNorm(p,μ,σ,LEFT)​

​c=invNorm(1−p,μ,σ,CENTER)​

​c=invNorm(p,μ,σ,RIGHT)​

Nice work completing Z-test and Confidence Intervals, here's a quick recap of what we covered:

Skills covered

Mixed Practice

Exercises checked off

I'm Plex, here to help you understand this concept!
/
Inference & Hypotheses
/
Z-test and Confidence Intervals
Further χ² tests & unbiased estimators
Z-test and Confidence Intervals
Inference & Hypotheses

Z-test and Confidence Intervals

0 of 0 exercises completed

Z-tests and confidence intervals for a population mean using technology, including Z-Test for one sample, two-sample and paired samples via differences with ​μ0​=0, normal confidence intervals when ​σ​ is known, t-intervals when ​σ​ is unknown, and the use of critical values, critical regions and confidence levels.

Want a deeper conceptual understanding? Try our interactive lesson!

Understanding confidence intervals
AHL AI 4.16

A confidence interval is a range of values in which we believe the true mean of the population lies.

When we take samples from a larger population, our samples tend to cluster around the mean of the population. Using a sample, we can make an educated guess that the true population mean is in some range. The more data we have, the better our guess gets.

We calculate this interval in line with the level of certainty we want to have that our range contains the true mean. Obviously, we can be ​100%​ sure that the value is between ​−∞​ and ​+∞, but the smaller the range, the lower the confidence level we can have.

Normal confidence interval using technology
AHL AI 4.16

If we know the standard deviation of the population whose mean ​μ​ we want a confidence interval for, we use a so called normal confidence interval.


Your calculator should include a statistical test called Zinterval or similar. To use it:

  1. Enter the value of ​σ, which must be known for a Z-test of any kind.

  2. Enter either

    • Data: a list of values you've typed into the calculator

    • Stats: the sample mean ​xˉ​ and ​n, the number of samples.

  3. Enter the confidence level and hit calculate

The calculator returns the desired interval, which is symmetrical around ​xˉ.

T-interval confidence interval using technology
AHL AI 4.16

To find a confidence interval for the mean without knowing the variance, we first have to use our sample to first estimate the standard deviation. Because of this extra uncertainty, we switch to using a t-distribution.


Your calculator should include a statistical test called Tinterval or similar. To use it:

  1. Enter either

    • Data: a list of values you've typed into the calculator

    • Stats: the sample mean ​xˉ, the sample standard deviation ​Sx​ and ​n, the number of samples.

  2. Enter the confidence level and hit calculate

The calculator returns the desired interval, which is symmetrical around ​xˉ.

Z-Test for population mean
AHL AI 4.18

Z-tests allow us to test the mean of a sample against

  • a population with known mean: use Z-Test

  • another sample: use 2-SampZTest

  • a paired sample: calculate the difference, then use Z-Test with ​μ0​=0.

When testing for the mean, or finding confidence intervals, we have to decide between using a T-test or a Z-test:

  • We use ​Z​ (normal) when the standard deviation is known.

  • We use ​T​ when the standard deviation is not known.

The ​T​-distribution is very close to the normal, except that is slightly wider to account for the fact that we are estimating the standard deviation using a sample.

Critical values & regions
AHL AI 4.18

When testing the mean of a sample against a population, the critical region is the set of values for the sample mean that would lead to rejecting the null hypothesis. The critical value(s) is (are) the boundary of the critical region. In other words, the critical value is the threshold for ​xˉ​ that leads to a ​p​ value exactly equal to the chosen significance level.

​μ<μ0​​

​μ=μ0​​

​μ0​<μ​

​c=invNorm(p,μ,σ,LEFT)​

​c=invNorm(1−p,μ,σ,CENTER)​

​c=invNorm(p,μ,σ,RIGHT)​

Nice work completing Z-test and Confidence Intervals, here's a quick recap of what we covered:

Skills covered

Mixed Practice

Exercises checked off

I'm Plex, here to help you understand this concept!

Generating starter questions...

1 free

Generating starter questions...

1 free