Terms used in Statistical Analysis

The father of NHST never endorsed, nevertheless, the inflexible utility of the ultimately subjective threshold ranges nearly universally adopted afterward (though the introduction of the zero.05 has his paternity additionally). If you are testing it to see if it kills beetle larvae, you understand there’s a fairly good probability it’ll work, so you can be fairly sure that a P value lower than 0.05 is a true positive. But if you’re testing that one plant extract to see if it grows hair, which you realize may be very unlikely , a P worth less than 0.05 is sort of actually a false positive. In different phrases, when you count on that the null speculation is probably true, a statistically significant result might be a false constructive.

Universe – Universe is synonymous with population and is found primarily in older statistical textbooks. Majority of the newer textbooks and statistical literature use population to define the experimental units of primary interest. Uniform distribution – Uniform distributions are appropriate for cases when the probability of achieving an outcome within a range of outcomes is constant. An example is the probability of observing a crash at a specific location between two consecutive post miles on a homogenous section of freeway. Test of hypothesis – It is a statistical test of the plausibility of the null hypothesis in a study. T-score – It is a standard score derived from a z-score by multiplying the z-score by 10 and adding 50.

Although it is intuitively simpler than the standard deviation it is used less. The reason is largely since the standard deviation is used in inference, because the population standard deviation is one of the parameters of the normal distribution. Homogeneity – This term is used in statistics to describe samples or individuals from populations, which are similar with respect to the phenomenon of interest.

Statistical model – It is a set of one or more equations describing the process or processes which generated the scores on the study end point. Spearman’s rank order correlation – It is a non-parametric test used to measure the relationship between two rank ordered scales. Skew – If the distribution (or ‘shape’) of a variable is not symmetrical about the median or the mean it is said to be skew. The distribution has positive skewness if the tail of high values is longer than the tail of low values, and negative skewness if the reverse is true. Sensitivity analysis – It is an alternative analysis using a different model or different assumptions to explore whether one’s main findings are robust to different analytical approaches to the study problem.

Type II error – It is the probability of failing to reject a false null hypothesis in a statistical test. If, as the result of a test statistic computed on sample data, a statistical hypothesis is accepted when it is false, i.e., when it should have been rejected, then a type II error has been made. Beta is pre-selected by the analyst to determine the type II error rate. There are two schools of thought in statistical inference, classical or frequentist statistics for which RA Fisher is considered to be the founding father, and Bayesian inference, discovered by a man bearing the same name.

The data is displayed as a collection of points, each having the value of one variable determining the position on the horizontal axis and the value of the other variable determining the position on the vertical axis . Random variable – It is a variable whose exact value is not known prior to measurement. Typically, independent variables in experiments are not random variables since their values are assigned or controlled by the analyst.

What is a Hypothesis?

Frequently, repeated-measures ANOVA features a treatment factor and time as the two explanatory variables. Observational data – Observational data are non-experimental data, and there is no control of potential confounding variables in the study. Null hypothesis – The null hypothesis represents a theory which has been put forward, normally as a basis for argument. The null hypothesis is normally simpler than the alternative hypothesis and is given special consideration.

Where the graph also includes a category variable, a separate line can be drawn for each level of this variable. In some cases, they can coincide completely, so obscuring some of the points. A solution is to randomly move the dots perpendicularly from the axis, to separate them from one another. Internal validity – It is the extent to which treatment-group differences on a study endpoint represent the causal effect of the treatment on the study endpoint. Dot plot – A dot-plot is an alternative to a boxplot where each value is recorded as a dot.

Pilot surveys are an important step in the survey process, specifically for removing unintentional survey question biases, clarifying ambiguous questions, and for identifying gaps and / or inconsistencies in the survey instrument. Non-linear relation – A non-linear relation is one where a scatter plot between two variables X1 and X2 does not produce a straight-line trend. In several cases a linear trend can be observed between two variables by transforming the scale of one or both variables.

What is statistical hypothesis testing?

Various who is known as the father of null hypothesiss used in the statistical analysis along with their definitions are given below. In general, investigations and analyses of statistics fall into two broad categories called descriptive and inferential statistics. Descriptive statistics deals with the processing of data without attempting to draw any inferences from it. It involves the tabulating, depicting, and describing of the collections of data. The data provide a picture or description of the properties of data collected in order to summarize them into manageable form.

• This process is repeated to create multiple copies of one’s data; then one’s statistical analysis of the data is repeated with each copy of the dataset and the results are combined into one final set of results.
• An average (e.g., mean or median) and a measure of spread, (e.g., standard deviation or quartiles) are frequently used to summarize a numerical variable.
• He defines optimum as the minimum time necessary to yield comestible fruits.
• The collective views of a large number of people, especially on some particular topic.

That is, if an unbiased estimator is shown to be equivalent to the Cramer-Rao bound, then there are no other unbiased estimators which are more efficient. It is possible in some cases to find a more efficient estimate of a population parameter which is biased. Discrete variable – A set of data is discrete if the values belonging to it are distinct, i.e., they can be counted. Examples are the number of children in a family, the number of rainy days in the month, or the length of the longest dry spell in the growing season. A discrete variable is measured on the nominal or ordinal scale, and can assume a finite number of values within an interval or range.

What is Hypothesis Testing?

Examples of ordinal variables include the choice between three automobile brands, where the response is highly desirable, desirable, and least desirable. Ordinal variables provide the second lowest quantity of information compared to other scales of measurement. Nominal scale – It is a variable measured on a nominal scale which is the same as a categorical variable. The nominal scale lacks order and does not possess even intervals between levels of the variable.

Dummy variable – It is a variable in a regression model coded 1 if the case falls into a certain category of an explanatory variable and 0 otherwise. Dispersion of a distribution – It is the degree of spread shown by a variable’s values, typically assessed with the standard deviation. Deviation score – It is the difference between a variable’s value and the mean of the variable. Directional conclusion – It is a conclusion in a two-tailed test which uses the nature of the sample results to suggest where the true parameter lies in relation to the null hypothesized value.

Interest centres on describing the average trajectory of change, as well as what subject characteristics lead to different trajectories of change for different types of subjects. Exogenous variables – An exogenous variable in a statistical model refers to a variable whose value is determined by influences outside of the statistical model. An assumption of statistical modelling is that explanatory variables are exogenous.

Your data should come from the concerned population for which you want to make a hypothesis. Often after formulating research statements, the validity of those statements need to be verified. Hypothesis testing offers a statistical approach to the researcher about the theoretical assumptions he/she made. It can be understood as quantitative results for a qualitative problem.

For example, a scatter plot of log and X2 can produce a linear trend. In this case the variables are said to be non-linearly related in their original scales, but linear in transformed scale of X1. Missing data – It is the problem of data being absent for one or more variables in one’s study.

Estimates obtained using this method are called maximum likelihood estimates. Confidence interval – It is an interval of numbers which people are very confident contains the true value of a population parameter. A confidence interval gives an estimated range of values which is likely to include an unknown population parameter. The width of the confidence interval gives an idea of how uncertain people about the unknown parameter.

This type of error is known truth inflation (true size of the effect gets inflated. It arises in small sample size studies which are underpowered ). But what prompted the ASA for the very first time to issue such a statement which deals with the specific matters of statistical practice. The p-value is defined as the probability, under the assumption of no effect or no difference , of obtaining a result equal to or more extreme than what you actually observe. Hypothesis testing in statistics refers to analyzing an assumption about a population parameter. It is used to make an educated guess about an assumption using statistics.

In addition, errors occur in data collection, sometimes resulting in outlying observations. Finally, type I and type II errors refer to specific interpretive errors made when analyzing the results of hypothesis tests. Descriptive statistics – It is the body of statistical techniques concerned with describing the salient features of the variables used in one’s study. If one has a large set of data, then descriptive statistics provides graphical (e.g., boxplots) and numerical (e.g., summary tables, means, quartiles) ways to make sense of the data.

The power is greatest when the probability of a Type II error is least. Post-hoc theorizing – Post hoc theorizing is likely to occur when the analyst attempts to explain analysis results after-the-fact. In this second-rate approach to scientific discovery, the analyst develops hypotheses to explain the data, instead of the converse . The number of post-hoc theories which can be developed to ‘fit’ the data is limited only by the imagination of a group of people. With an abundance of competing hypothesis, and little forethought as to which hypothesis can be afforded more credence, there is little in the way of statistical justification to prefer one hypothesis to another. More importantly, there is little evidence to eliminate the prospect of illusory correlation.

At the very least, your experiment will not be considered seriously. But the speculation test is designed as a method to determine between A and B, so the result of this test is to just accept one of these two hypotheses . This strategy requires some additional thoughts and considerations that finally lead to the choice of sensible error-rates and the dedication of the required sample dimension. The first step is for the analyst to state the two hypotheses in order that only one could be proper. The subsequent step is to formulate an analysis plan, which outlines how the info will be evaluated. Transformation – A transformation is the change in the scale of a variable.

Not even in circumstances where there isn’t a proof that the null hypothesis is false is it valid to conclude the null speculation is true. If the null hypothesis is that µ1 – µ2 is zero then the speculation is that the difference is exactly zero. No experiment can distinguish between the case of no distinction between means and a particularly small distinction between means. Weight – It is a numerical coefficient attached to an observation, frequently by multiplication, in order that it assumes a desired degree of importance in a function of all the observations of the set.

White noise – For time series analysis, white noise is defined as a series whose elements are uncorrelated and normally distributed with mean zero and constant variance. The residuals from properly specified and estimated time series models are tobe white noise. Science – Science is the accumulation of knowledge acquired by careful observation, by deduction of the laws which govern changes and conditions, and by testing these deductions by experiment. The scientific method is the corner-stone of science, and is the primary mechanism by which scientists make statements about the universe and phenomenon within it. Reverse causation – It is the situation in which the study end point in a regression model is actually the cause of one of the explanatory variables in the model, rather than the other way around.