The Niagara Employment Inventory dataset from 2022. Feature layer containing data points from the Niagara Employment Inventory. Containing only open businesses as of 2022
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Last Updated May 2, 2023, 14:31
Created May 2, 2023, 14:30
contact John Docker
contact_email john.docker@regional.niagara.on.ca
geography Niagara Region,
metadata_url
progress Complete
publication_date 05/01/2022 00:00:00
released 2022-05-01
update_frequency Annually
Seems like dummy data uploaded to https://niagaraopendata.ca/dataset/2022-niagara-employment-inventory for dummy tests ig idk
TODO: Statistical hypothesis: A statement about the parameters describing a population (not a sample). Test statistic: A value calculated from a sample without any unknown parameters, often to summarize the sample for comparison purposes. Simple hypothesis: Any hypothesis which specifies the population distribution completely. Composite hypothesis: Any hypothesis which does not specify the population distribution completely. Null hypothesis (H0) Positive data: Data that enable the investigator to reject a null hypothesis. Alternative hypothesis (H1) Region of rejection / Critical region: The set of values of the test statistic for which the null hypothesis is rejected. Critical value Power of a test (1 − β) Size: For simple hypotheses, this is the test's probability of incorrectly rejecting the null hypothesis. The false positive rate. For composite hypotheses this is the supremum of the probability of rejecting the null hypothesis over all cases covered by the null hypothesis. The complement of the false positive rate is termed specificity in biostatistics. ("This is a specific test. Because the result is positive, we can confidently say that the patient has the condition.") See sensitivity and specificity and Type I and type II errors for exhaustive definitions. Significance level of a test (α) p-value Statistical significance test: A predecessor to the statistical hypothesis test (see the Origins section). An experimental result was said to be statistically significant if a sample was sufficiently inconsistent with the (null) hypothesis. This was variously considered common sense, a pragmatic heuristic for identifying meaningful experimental results, a convention establishing a threshold of statistical evidence or a method for drawing conclusions from data. The statistical hypothesis test added mathematical rigor and philosophical consistency to the concept by making the alternative hypothesis explicit. The term is loosely used for the modern version which is now part of statistical hypothesis testing. Conservative test: A test is conservative if, when constructed for a given nominal significance level, the true probability of incorrectly rejecting the null hypothesis is never greater than the nominal level. Exact test