Search results
The following table documents the most notable of these in the context of probability and statistics — along with each symbol’s usage and meaning. If A ⊥ B and P (A) ≠ 0, then P (B ∣ A) = P (B). If E 1 E 2, then P (E 2 | E 1) ≥ P (E 2). Bin (n, p).
- Logic Symbols
Additional Resources. Definitive Guide to Learning Higher...
- Set Theory Symbols
S et theory is a branch of mathematics dedicated to the...
- Calculus and Analysis Symbols
Probability and Statistics Symbols; Calculus and Analysis...
- Algebra Symbols
A lgebra is a subfield of mathematics pertaining to the...
- Math Vault
I n basic mathematics, many different symbols exist and are...
- Geometry and Trigonometry Symbols
G eometry and trigonometry are branches of mathematics...
- Greek, Hebrew, Latin-based Symbols
T he field of mathematics customarily uses letters as...
- DMCA
Higher Math Proficiency Test; 10 Commandments of Higher Math...
- Logic Symbols
- How Do You Test For Statistical significance?
- What Is A Significance level?
- Problems with Relying on Statistical Significance
- Other Types of Significance in Research
- Other Interesting Articles
In quantitative research, data are analyzed through null hypothesis significance testing, or hypothesis testing. This is a formal procedure for assessing whether a relationship between variables or a difference between groups is statistically significant.
The significance level, or alpha (α), is a value that the researcher sets in advance as the threshold for statistical significance. It is the maximum risk of making a false positive conclusion (Type I error) that you are willing to accept. In a hypothesis test, the pvalue is compared to the significance level to decide whether to reject the null hy...
There are various critiques of the concept of statistical significance and how it is used in research. Researchers classify results as statistically significant or non-significant using a conventional threshold that lacks any theoretical or practical basis. This means that even a tiny 0.001 decrease in a pvalue can convert a research finding from s...
Aside from statistical significance, clinical significance and practical significance are also important research outcomes. Practical significance shows you whether the research outcome is important enough to be meaningful in the real world. It’s indicated by the effect sizeof the study. Clinical significanceis relevant for intervention and treatme...
If you want to know more about statistics, methodology, or research bias, make sure to check out some of our other articles with explanations and examples.
The problem can run the other way. Scientists routinely judge whether a significant difference exists simply by eye, making use of plots like this one: Figure 5.2.1 5.2. 1. Imagine the two plotted points indicate the estimated time until recovery from some disease in two different groups of patients, each containing ten patients.
Oct 21, 2024 · This is generally considered an appropriate level of risk. However, if the alpha level was set to .50 (meaning 50%), for example, it would mean that the statistician was taking a 50% risk of a Type I Error; this would mean it could be just as likely that the hypothesis was wrong as that it was right.
Dec 11, 2020 · Using descriptive and inferential statistics, you can make two types of estimates about the population: point estimates and interval estimates. A point estimate is a single value estimate of a parameter. For instance, a sample mean is a point estimate of a population mean.
- The standard error of the mean , or simply standard error , indicates how different the population mean is likely to be from a sample mean. It tell...
- Standard error and standard deviation are both measures of variability . The standard deviation reflects variability within a sample, while the sta...
- Using descriptive and inferential statistics , you can make two types of estimates about the population : point estimates and interval estimates. A...
Mar 28, 2017 · Be especially sceptical of unlabelled graphs. Graphs can tell a story – making differences look bigger or smaller depending on scale. Statistics. Correlation. Causation. Statistics probability ...
Jan 18, 2021 · In statistics, power refers to the likelihood of a hypothesis test detecting a true effect if there is one. A statistically powerful test is more likely to reject a false negative (a Type II error). A statistically powerful test is more likely to reject a false negative (a Type II error).
