Stats test

What are the 2 types of descriptive statistics?

Central tendency and spread

Central tendency

Any measure that describes where the center of our distribution lies.

• Tells us what the “average” individual in our population is like.

• Includes mean, median, mode.

Spread

Any measure that describes how variable our population is.

• Determines how spread out our distribution is in the histogram.

Mean

The average.

• Most common measure of central tendency, calculated by summing all values and dividing by the number of values.

Median

The middle number if you list all your data points from smallest to largest.

Mode

The most common value

Histogram

A useful graph to explore data for a single variable at a time.

• It shows the number of times we observe a value for some variable within a bunch of “bins” or ranges of value

What are the 2 quantities of standard error?

Standard deviation and sample size

Descriptive statistics

Values that summarize and describe the main features of a population of interest (data).

NOT USED TO TEST A HYPOTHESIS.

Standard Deviation (SD)

A measure of the amount of variation in a set of values, indicating how far each data point is from the mean of the set.

Standard error (SE)

Helps us decide how confident we are that our experimental measurements (mean) actually represents the true population mean.

If standard deviation gets larger

Standard error gets larger, indicating we are less confident in our results.

If sample size gets larger

Standard error gets smaller, indicating more confidence in our results.

Variance

Standard deviation squared.

A distribution with a wider variance/standard deviation will have a wider spread when plotted in a histogram

Inferential statistics

Also called hypothesis tests, they are used to test a hypothesis about your data in order to make a scientific inference

Alternative Hypothesis

A prediction that pairs with your null hypothesis and says that something is happening with your data.

Inferential statistics

Data analysis techniques used to statistically test a hypothesis about your data in order to make a scientific inference.

Null Hypothesis

A prediction that is used in the context of a statistical test stating that nothing happens.

P-value

The probability of getting a result like the obserbed data if the null hypothesis is true.

Sample size

The number of experimental observations

Hypothesis test

Data analysis techniques used to statistically test a hypothesis about your data.

T-test

A type of inferential statistical analysis used to test for a difference in the average value between two groups.

Degrees of freedom

A meausure of how much information you have. It is typically calculated as the sample size minus one.

Barplots

Display categorical data on X-axis and continuous data on the Y-axis