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Glossary

6

68% Rule

Criticality: 3

In a normal distribution, approximately 68% of the data falls within one standard deviation of the mean.

Example:

If the average IQ is 100 with a standard deviation of 15, then about 68% of people have an IQ between 85 and 115 according to the 68% Rule.

9

95% Rule

Criticality: 3

In a normal distribution, approximately 95% of the data falls within two standard deviations of the mean.

Example:

Using the IQ example, the 95% Rule suggests that about 95% of people have an IQ between 70 and 130.

B

Bimodal Distribution

Criticality: 2

A frequency distribution that has two distinct peaks or modes, suggesting the presence of two different groups or categories within the data.

Example:

A survey on preferred sleep times might show a bimodal distribution if there are two common preferences, like early risers and night owls.

C

Correlation

Criticality: 3

A statistical measure that describes the strength and direction of a relationship between two variables.

Example:

Observing that students who spend more time studying tend to get higher grades suggests a correlation between study time and academic performance.

Correlation Coefficient

Criticality: 3

A numerical index that quantifies the strength and direction of a linear relationship between two variables, ranging from -1 to +1.

Example:

A correlation coefficient of +0.9 indicates a very strong positive relationship, like between hours spent exercising and calories burned.

Correlation vs. Causation

Criticality: 3

A critical distinction in research, emphasizing that just because two variables are related (correlated) does not mean one causes the other.

Example:

Finding a correlation vs. causation between ice cream sales and crime rates doesn't mean ice cream causes crime; both might increase in hot weather.

D

Descriptive Statistics

Criticality: 3

Statistical methods used to summarize and describe the main features of a dataset, such as measures of central tendency and variation.

Example:

Calculating the average score of a class on a psychology exam is an example of using descriptive statistics to understand the class's performance.

F

Frequency Distribution

Criticality: 2

A summary of how often different scores or values occur in a dataset, often displayed in tables or graphs.

Example:

A psychologist might create a frequency distribution to show how many students scored within specific ranges on a personality test.

I

Inferential Statistics

Criticality: 2

Statistical methods used to make generalizations or draw conclusions about a larger population based on data collected from a sample.

Example:

A researcher uses inferential statistics to determine if the results from a study on a small group of participants can be applied to all teenagers.

M

Mean

Criticality: 3

The arithmetic average of a dataset, calculated by summing all values and dividing by the number of values.

Example:

If a student's test scores are 80, 90, and 70, their mean score is 80.

Median

Criticality: 3

The middle value in a dataset when the values are arranged in numerical order.

Example:

In the dataset 1, 3, 5, 8, 10, the median is 5, as it's the central value.

Mode

Criticality: 2

The value that appears most frequently in a dataset.

Example:

In a survey where most people chose 'blue' as their favorite color, 'blue' would be the mode.

N

Negative Correlation

Criticality: 3

A relationship between two variables where as one variable increases, the other tends to decrease.

Example:

A negative correlation might exist between the number of hours spent watching TV and academic grades, where more TV time is associated with lower grades.

Negatively Skewed

Criticality: 3

A distribution where the tail extends to the left, indicating that most scores are higher, and the median is typically greater than the mean.

Example:

The distribution of scores on an easy exam might be negatively skewed, with most students scoring high and only a few scoring low.

No Correlation

Criticality: 2

A lack of a consistent relationship between two variables, meaning changes in one variable are not predictably associated with changes in the other.

Example:

There is typically no correlation between a person's shoe size and their intelligence level.

Normal Distribution

Criticality: 3

A symmetrical, bell-shaped curve that represents the distribution of many natural phenomena, where most data points cluster around the mean.

Example:

Human height often follows a normal distribution, with most people being of average height and fewer people being extremely tall or short.

P

Positive Correlation

Criticality: 3

A relationship between two variables where both variables tend to increase or decrease together.

Example:

There is a positive correlation between the amount of time spent practicing a musical instrument and proficiency in playing it.

Positively Skewed

Criticality: 3

A distribution where the tail extends to the right, indicating that most scores are lower, and the mean is typically greater than the median.

Example:

Income distribution in many countries is often positively skewed, with most people earning lower incomes and a few earning very high incomes.

R

Range

Criticality: 2

The difference between the highest and lowest values in a dataset.

Example:

If the highest score on a quiz was 95 and the lowest was 60, the range of scores is 35.

S

Standard Deviation

Criticality: 3

A measure of the average amount by which scores in a dataset deviate from the mean, indicating the spread or variability of the data.

Example:

A low standard deviation in test scores means most students scored very close to the class average, indicating consistent performance.

Statistical Significance

Criticality: 3

A determination that a research result is unlikely to have occurred by chance, suggesting a real effect or relationship.

Example:

A drug trial showing a statistical significance in reducing symptoms means the improvement is likely due to the drug, not just random variation.