What is 'Statistical Significance'
Statistical significance means that a result from testing or experimenting is not likely to occur randomly or by chance, but is instead likely to be attributable to a specific cause. Statistical significance can be strong or weak, and it is important to disciplines that rely heavily on analyzing data and research, such as finance, investing, medicine, physics and biology.
!breakWhen analyzing a data set and doing the necessary tests to discern whether one or more variables have an effect on an outcome, statistical significance helps show that the results from the test are real and not just by luck or chance. Problems arise in tests of statistical significance because researchers are usually working with samples of larger populations. As a result, the samples must be representative of the population, so the data contained in the sample must not be biased in any way.
Statistical significance does not always indicate practical significance, meaning the results cannot be applied to realworld business situations. In addition, statistical significance can be misinterpreted when researchers do not use language carefully in reporting their results. Another problem that may arise with statistical significance is that past data, and the results from that data, whether statistically significant or not, may not reflect ongoing or future conditions. In investing, this may manifest itself in a pricing model breaking down during times of financial crisis as correlations change and variables do not interact as usual. Statistical significance can also help an investor discern whether one asset pricing model is better than another.
Calculating Statistical Significance
The calculation of statistical significance (significance testing) is subject to a certain degree of error. The researcher must define in advance the probability of a sampling error, which exists in any test that does not include the entire population. Sample size is an important component of statistical significance in that larger samples are less prone to flukes. Only random, representative samples should be used in significance testing. The level at which one can accept whether an event is statistically significant is known as the significance level. Researchers use a test statistic known as the pvalue to discern whether the event falls below the significance level; if it does, the result is statistically significant. The pvalue is a function of the means and standard deviations of the data samples.
The pvalue indicates the probability under which a statistical result occurred by chance or by sampling error. In other words, the pvalue indicates the risk that there is no actual difference or relationship. The pvalue must fall under the significance level for the results to at least be considered statistically significant. The opposite of the significance level, calculated as 1 minus the significance level, is the confidence level. The confidence level indicates the degree of confidence that the statistical result did not occur by chance or by sampling error. The customary confidence level in many statistical tests is 95%, leading to a customary significance level or pvalue of 5%.
BREAKING DOWN 'Statistical Significance'

Statistically Significant
The likelihood that a result or relationship is caused by something ... 
Sample
A subset containing the characteristics of a larger population. ... 
Sampling Distribution
A probability distribution of a statistic obtained through a ... 
Sampling
A process used in statistical analysis in which a predetermined ... 
Standard Error
The standard deviation of the sampling distribution of a statistic. ... 
Statistics
A type of mathematical analysis involving the use of quantified ...

Investing
Understanding Statistics
Statistics provide the means to analyze data and then summarize it into a numerical form. 
Investing
Explaining Standard Error
Standard error is a statistical term that measures the accuracy with which a sample represents a population. 
Investing
How Statistical Significance is Determined
If something is statistically significant, itâ€™s unlikely that it happened by chance. 
Investing
How Does Sampling Work?
Sampling is a term used in statistics that describes methods of selecting a predefined representative number of data from a larger data population. 
Investing
What is a Representative Sample?
In statistics, a representative sample accurately represents the makeup of various subgroups in an entire data pool. 
Investing
What is Systematic Sampling?
Systematic sampling is similar to random sampling, but it uses a pattern for the selection of the sample. 
Investing
Understanding the Simple Random Sample
A simple random sample is a subset of a statistical population in which each member of the subset has an equal probability of being chosen. 
Insights
Can Investors Trust Official Statistics?
The official statistics in some countries need to be taken with a grain of salt. Find out why you should be skeptical. 
Investing
What is Descriptive Statistics?
Descriptive statistics is the term applied to meaningful data analysis. 
Investing
Hypothesis Testing in Finance: Concept & Examples
When you're indecisive about an investment, the best way to keep a cool head might be test various hypotheses using the most relevant statistics.

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