what is icecream? 

Mode

Hypothesis

Falsifiable statement (testable), regarding the true state of affairs in the world

Scientific law

Describes the world. Prediction if this than that (no insight) 

Scientific Method:

Building a theory

1.    Observation: Patterns, ask questions

2.    Codification: We give names to things

3.    Formulate a hypothesis

4.    Test Hypothesis – Remember that we can never prove a hypothesis

5.    Again: Over and over again (Rinse and Repeat) 

Causality:

Causal association, study design

1.    Temporal order- Cause and effect

2.    Co-variation- Things change together, cause present to effect

3.    Rule of alternative explanations

Population

 Group of entities if interest, described with Greek letters μ (mu) 

Sample:

Subset from a population 

Statistic:

Describes characteristics of samples, described by Latin/ Roman letters 

Descriptive Statistic:

Describes sample and sample only

Inferential statistic:

Sample is like the population, ascribe value to the population

Measurements:

Assignment of a value based on a phenomena based on a preset set of rules 

Nominal:

Data gives names to things, mutually excusive. Ex green, blue

Ordinal:

Sequence/ magnitude is not relevant Example: Olympic medals

Interval:

Equidistance, zero arbitrary Example: Fahrenheit vs. Celsius 

Ratio:

Absolute zero, absence of what we are measuring ex age, distance 

Operational definition:

Defines something tangible 

Datum:

a fixed starting point of a scale or operation.

         Example: Let the symbol X_ij denote the ith datum in the jth group of data

                  1^st datum in the 2^nd group = X₁₂

Deviate:

Distance and direction from the mean 

Coefficient of Alienation: 

1 - r²

the proportion of variance in the dependent variable that is not accounted for by the independent variable(s)

Frequency distributions: 

1.    Frequency polygon

2.    Stem and leaf plot- Numbers turn into information from data 

Kurtosis:

Measure of tails – variance = thickness of tails

[image] 

NHST 

null hypothesis statistical testing

f-distribution 

2 parameters

Separate in half

square root of the variance 

S²

standard deviation 

reason why the devisor of the variance of n-1

un-bias estimate – the reason we divide n-1 if we don’t we will underestimate the population variance- when we systematically do something wrong then it is known as bias- skewed 

alpha error

Conclusion validity 

degree to which inferences are useful and meaningful

Power

the probability of rejecting a false null hyposthesis

Linear regression attempts to model the relationship between two variables by fitting a linear equation to observed data.

[image]

type 2 error

hypothesis is forced and failed to be rejected, beta 

True or false – the normal distribution has a mean of zero

3 things necessary to establish causal relation

temporal order, covariance, and no rule out alternative 

frequency-# of times it appears 

cf- cumilative frequences (add them all up)

p- porportion 

cp- cumilative proportion 

Strength of relationship, represented by r

0 to 1 (one means extremely strong relationship, doesnt matter about negative) 

Measure of the spread of data

squared distance between each point and the mean/ n-1

 [image]

standard deviation 

[image]