Statistical Significance
Statistical Significance - Related Videos
01:37
Statistical Significance
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2026
Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this...
06:02
Visual Statistical Learning
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2023
Source: Laboratory of Jonathan Flombaum—Johns Hopkins University The visual environment contains massive amounts of information involving the relations between objects in space and time; certain objects are more likely to appear in the vicinity of other objects. Learning these regularities can support a wide array of visual processing, including object recognition. Unsurprisingly, then, humans appear to learn these regularities automatically, quickly, and without conscious awareness. The name...
01:21
Significance of Marketing
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2024
Marketing is a pivotal, multifaceted function that acts as a strategic bridge between businesses and customers, extending beyond advertising and sales to contribute significantly to organizational success. Marketing guides businesses in: Market Understanding: This involves extensive research to comprehend the target audience's needs, forming the basis for product development, pricing, and communication strategies. Customer Acquisition & Retention: It plays a pivotal role in attracting...
01:14
Probability in Statistics
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2023
Probability is the likelihood of an event occurring. The term event is defined as a collection of results of a procedure. An event is a simple event when an outcome cannot be divided into simpler parts. An example of a simple event is a coin toss. The result of a coin toss is either a head or a tail. Here, head and tail are two simple events. These two simple events make up the sample space. Further, the probability of an event occurring falls within the range of 0 to 1. The probability of an...
01:12
Significance of Center of Mass
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2023
The center of mass of an object is defined as the mass-weighted average position of all the particles that comprise the object. The significance of the center of mass of an object can be seen by looking at its dynamics. The time derivative of the center of mass gives its velocity, assuming that the object's mass remains constant over time. Furthermore, the total linear momentum of an object can be seen as the linear momentum of a single particle of the object's total mass moving with the...
FAQs
A p-value quantifies how compatible an observed result is with the specified null hypothesis when sampling variability is considered. It is derived from a test statistic, so its interpretation depends on both the chosen hypothesis and the data-generating variation represented by the test. A small p-value supports rejecting that null under the selected decision rule, not proving an alternative explanation.
The significance level sets the decision threshold before interpreting the p-value. With a commonly used level of 0.05, a p-value below that level leads researchers to reject the null hypothesis, whereas a p-value at or above it does not provide that decision basis. The threshold is a rule for hypothesis testing, not a universal boundary between important and unimportant findings.
Sampling variability is central because repeated samples can produce different observed results even when investigators evaluate the same null hypothesis. Statistical significance therefore reflects the result’s position relative to the variability expected under that hypothesis. A finding may reach significance in one sample and not another, so interpretation should consider study design, effect size, confidence intervals, and domain context rather than the p-value alone.
Statistical significance and practical importance answer different questions. The former concerns whether the observed result is unusual under the null hypothesis, while the latter concerns whether the size of the result matters in the relevant domain. Reporting effect sizes and confidence intervals helps show the magnitude and precision of an estimate, preventing a threshold-based decision from carrying more meaning than the data support.
A basic analysis begins with a specified null hypothesis and an observed result, then uses an appropriate test statistic to calculate a p-value. Researchers compare that value with the selected significance level and record the resulting decision about the null hypothesis. The report should then add effect sizes, confidence intervals, study-design information, and domain context so readers can assess the finding more fully.
In statistical comparisons, significance testing helps assess whether observed differences are unlikely under a specified null hypothesis. The same framework can contribute to model evaluation and to interpretation of experimental or observational data. Its role is evaluative rather than conclusive: the result does not by itself establish causal truth, and observational findings require the surrounding study design and context for responsible interpretation.