Professional Responsibility, Biomechanics: Errors, Variability and Statistics

Flashcards covering key vocabulary from the lecture notes on Professional Responsibility, Biomechanics, Errors, Variability, and Statistics.

Mean

The average of all the numbers that constitute your data.

Standard Deviation (SD)

Measure of the extent to which raw data deviates from the mean; defined as the root of the mean of the sums of the squares of the deviations.

Null Hypothesis

An explanation against your anticipated cause and effect, based on the action of chance alone, assumes nothing special is happening.

Probability

A measure of the likelihood of a specific event occurring, crucial for interpreting cause and effect in experiments.

Poisson Distribution

A discrete probability distribution that expresses the probability of a given number of events occurring in a fixed interval of time or space if these events occur with a known constant mean rate and independently of the time since the last event.

Gaussian Distribution

A continuous probability distribution, also known as the normal distribution, often used as an approximation of probability; it demonstrates a bell shape curve when graphed.

Normal Curve

A specific probabilistic case of the Gaussian distribution where the area under the curve equals 1, characterized by its mean (μ) and standard deviation (σ).

Parametric Statistics

A branch of statistics that relies on specific assumptions about the underlying distribution of the data (e.g., Normal distribution), using parameters like mean and standard deviation to characterize the data.

Median

The 50th percentile of a dataset, representing the middle value when the data is ordered.

Mode

The most frequently occurring value in a dataset.

Central Limit Theorem

States that the distribution of sample means approaches a normal distribution as the sample size increases, regardless of the shape of the population distribution.

Standard Error of the Mean (SEM)

The standard deviation of the sampling distribution of the mean, estimated by the sample standard deviation divided by the square root of the sample size (n).

P-value

The probability of obtaining results as extreme as, or more extreme than, the observed results of a statistical hypothesis test, assuming that the null hypothesis is correct.