# Statistics

View:
Sorted by:

### Displaying Your Statistical Data with Charts and Graphs

You can summarize your statistical data in a visual way using charts and graphs. These are displays that are organized to give you a big picture of the data in a flash and to zoom in on a particular result

### Why Margin of Error and Confidence Intervals Matter in Statistics

Statistical results should always include a margin of error and confidence intervals. This information is important because you often see statistics that try to estimate numbers pertaining to an entire

### How Hypothesis Tests Are Used in Statistics

One main staple of research studies is called hypothesis testing. A hypothesis test is a technique for using data to validate or invalidate a claim about a population. For example, a politician may claim

### Avoid Drawing the Wrong Conclusions from Statistical Data

Statistical formulas don’t know whether they are being used properly, and they don’t warn you when your results are incorrect. In order to draw the appropriate conclusions, it’s up to you to avoid overstating

### How to Spot Mathematical Errors in Statistical Data

After examining the design of the study and how data was collected, the next thing to do when you come upon a statistic or the result of a statistical study is to look for mathematical errors in the data

### Types of Statistical Data: Numerical, Categorical, and Ordinal

When working with statistics, it’s important to recognize the different types of data: numerical (discrete and continuous), categorical, and ordinal. Data

### What a p-Value Tells You about Statistical Data

When you perform a hypothesis test in statistics, a p-value helps you determine the significance of your results. Hypothesis tests are used to test the validity of a claim that is made about a population

### How Statistical Correlation and Causation Are Different

Of all of the misunderstood statistical issues, the one that’s perhaps the most problematic is the misuse of the concepts of correlation and causation.

### How to Break Down Categorical Statistics Using Two-Way Tables

You can break categorical data down using two-way tables (also known as contingency tables, cross-tabulations or crosstabs) to summarize statistical information about different groups.

### How to Calculate the Mean of a Statistical Data Set

The most common way to summarize a statistical data set is to describe where the center, or mean, is. One way of thinking about what the center of a data set means is to ask, “What’s a typical value?”

### How to Find the Median Value in a Statistical Data Set

The median is a statistic that is commonly used to measure the center of a data set. However, it is still an unsung hero of statistics in the sense that it isn’t used nearly as often as it should be, although

### How to Identify Skew and Symmetry in a Statistical Histogram

Sometimes the mean versus median debate can get quite interesting. Especially when you look at the skewness and symmetry of your statistical data in a histogram.

### How to Interpret Standard Deviation in a Statistical Data Set

Standard deviation can be difficult to interpret as a single number on its own. Basically, a small standard deviation means that the values in a statistical data set are close to the mean of the data set

### Why Standard Deviation Is an Important Statistic

The standard deviation is a commonly used statistic, but it doesn’t often get the attention it deserves. Although the mean and median are out there in common sight in the everyday media, you rarely see

### Applying the Empirical Rule (68-95-99.7) to a Statistical Data Set

The Empirical Rule (68-95-99.7) says that if the population of a statistical data set has a normal distribution (where the data are in the shape of a bell curve) with population mean

### How to Calculate Percentiles in Statistics

If all you are interested in is where you stand compared to the rest of the herd, you need a statistic that reports relative standing, and that statistic is called a percentile. The

### What Percentile Tells You about a Statistical Value

Percentiles report the relative standing of a particular value within a statistical data set. If that’s what you’re most interested in, the actual mean and standard deviation of the data set are not important

### How to Gather a Five-Number Summary from a Statistical Sample

If your data create a histogram that is not bell-shaped, you can use a set of statistics that is based on percentiles to describe the big picture of the data. Called the five-number summary, this method

### How the Central Limit Theorem Is Used in Statistics

The normal distribution is used to help measure the accuracy of many statistics, including the sample mean, using an important result called the Central Limit Theorem.

### How Treatment Groups, Control Groups, Placebos, and Blind Experiments Are Used in Statistics

Statistical studies often involve several kinds of experiments: treatment groups, control groups, placebos, and blind and double-blind tests. An experiment

### How to Interpret the Margin of Error in Statistics

You’ve probably heard or seen results like this: “This statistical survey had a margin of error of plus or minus 3 percentage points.” What does this mean? Most surveys are based on information collected

### What a Boxplot Can Tell You about a Statistical Data Set

A boxplot can give you information regarding the shape, variability, and center (or median) of a statistical data set. It is particularly useful for displaying skewed data.

### What a Time Chart Can Tell You about a Statistical Data Set

A time chart (also called a line graph) is a statistical display used to examine trends in data over time (also known as time series data). Time charts show time on the

### How a Time Chart Can Misrepresent Statistical Data

When you create a statistical time chart, you have to evaluate the units of the numbers being plotted. For example, it's misleading to chart the number

### Simplifying Excess Statistical Data in a Time Chart

If a time chart includes too much statistical data, the result can be so complex that it makes it impossible to interpret the data. By reducing the amount of data, it is easier to see patterns emerge from