Differentiate between Correlation and Causation

Correlation and Causation
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Correlation and Causation
Differentiate between correlation and causation
Correlation refers to a reciprocal relationship between two or more variables or series of things, and the correlation coefficient (r) determines how strong the linear relationship is. In this case, there can be a positive correlation (relationship) where the values or series move in the same direction; when one variable increases, the other falls in a negative correlation, and statistical correlation determines the type of relationship. 
Causation reflects a cause-and-effect relationship between two or more variables. Determining causation requires conducting a true experiment. Correlation does not imply causation as there can be causation without one variable causing the other variable, and care must be exercised not to interpret. Differentiating the statistical correlation and causal conclusions is necessary to evaluate how one variable affects another variable or more variables. 
Explain how each is calculated or tested.
Correlation (Pearson correlation coefficient) is a regression measure that quantifies the strength of the relationship between two variables by determining the degree of linear relationship variables, and the two variables are represented in a scatter diagram.
The correlation coefficient is calculated as:
Where:Cov (x; y): the covariance between the value "x" and "y".
σ (x): standard deviation of x
σ (y): standard deviation of y
ρ = -1 Negative perfect correlation
ρ = 0 There is no correlation
ρ = +1 positive perfect correlation
Positive correlation occurs when the value "x" rises, the value "y" also increases, and also with the same magnitude (+1) and if there is a negative correlation when the value "x" rises, and the value "y" falls. After determining the correlation, the test for causation requires running a true experiment where there is control of the variables and the researcher manipulates another variable. Manipulating variable relates to changing its value from one level to another and control ensures that no other variable influences the relationship (Gravette et al., 2018). The aim of the experiment is to show that changing one value of a variable causes the second variable to change.
Hypothesis testing experiments are types of analyses to determine causation. Hypotheses tests are used to evaluate whether a hypothesis is plausible and this is necessary before determining whether the conclusions is likely possible but a significant effect does not reflect a big effect.
Statistical significance and correlation