Statistics for the Terrified v5.0: what does it cover?

Statistics for the Terrified covers all the techniques required by undergraduate students and many researchers in a user-friendly, commonsense, non-mathematical and highly interactive way.

Version 5.0 is suitable for undergraduate students and many researchers.

The LITE edition covers all the most essential areas: it is aimed at students who are working towards passing a unit or module within their main area of study. It is not suitable for those who intend to pursue research themselves.

Each chapter of the tutorial begins with an outline of its aims and objectives, and finishes with a summary of the key points covered. Click on the links below for screenshots.

	v5.0 Demo	LITE Buy now
ANALYSIS MODULE: Analyse your own data		-
the basics: describe your data		-
group comparisons: perform a two-sample t test on your data		-
group comparisons: perform a oneway anova on your data		-
group comparisons: twoway anova		-
group comparisons: perform a Mann-Whitney test on your data		-
group comparisons: perform a Kruskal-Wallis test on your data		-
group comparisons: perform a twoway cross-classification on your data		-
repeated measurements: perform a paired t test on your data		-
repeated measurements: perform a signed-rank Wilcoxon test		-
repeated measurements: time series (smoothing)		-
classification tables: perform a chi-square test on your data		-
related measurements: linefit your data		-
TUTORIAL: Probability
aims and objectives
the fundamentals
probability notation
conditional probability
Bayes theorem helps untangle confused logic
summary
TUTORIAL: Basic numerical data description
aims and objectives
data types
average values - the mean and median introduced
the effect of outliers on the mean and median
the advantages and disadvantages of the mean and median
range and variance
standard deviation
datagame - explore how each point contributes to variance and sd
what is the coefficient of variation?		-
summary
TUTORIAL: Graphical data description
aims and objectives
frequency charts and histograms
histograms and distributions
introducing the normal curve
the normal curve shows relative likelihood
the normal curve is always centred above its mean
how standard deviation affects normal curve shape
normal distribution and calculating probabilities: datagame		-
dealing with data that isn't normally distributed
summary
TUTORIAL: Standard error and confidence intervals
aims and objectives
introduction to standard error
standard error measures accuracy
standard error decreases as standard deviation increases
accuracy increases as standard error decreases
how this looks with normal curves
how to calculate standard error in theory
how to calculate standard error in practice
isn't it cheating to use estimates in the calculation?
what is a confidence interval?
confidence intervals are based on standard error
a 95% CI contains the population mean 95% of the time
summary
TUTORIAL: What does p<0.05 really mean?
aims and objectives
what is hypothesis testing?
posing the hypothesis
introducing the null hypothesis
random sampling
what do you take into account?
the steps involved in hypothesis testing
the relationship between the test statistic and p values
how difficult is it to reject the null hypothesis?
the relationship between power and type II errors
power worsens as the alternative gets closer
making significance tests more powerful		-
summary
TUTORIAL: How to choose a test		-
aims and objectives		-
the essentials		-
four simple but essential concepts		-
groups		-
data patterns and appropriate tests		-
questions to ask		-
the test quiz		-
summary		-
TUTORIAL: Testing for differences between groups
aims and objectives
overview - tests for differences between group means
two-sample t test overview
two-sample t test datagame
ANOVA overview
oneway ANOVA (with datagame)
what is twoway analysis of variance?		-
twoway ANOVA - interpretation and datagame		-
non-parametric methods
Mann-Whitney test - datagame and explore
Kruskal-Wallis test - datagame and explore		-
summary
TUTORIAL: Analysing repeated measures
aims and objectives
what are repeated measures?
collapse the data
types of repeat measure studies
before/after tests
the paired t test
the paired t test datagame
Wilcoxon signed rank test overview
Wilcoxon signed rank test datagame
why area under a curve?		-
areas from the baseline and axis		-
datagame the areas		-
data patterns		-
time series: smoothing - moving averages		-
time series: exponential smoothing		-
summary
TUTORIAL: Analysing 2x2 classification tables
aims and objectives
what are classification tables?
what is risk?
the importance of relative risk
what are expected values
interpretation of discrepancies and relative risk
the chi-square test and sample size
datagame the chi-square test
summary
TUTORIAL: Fitting lines to data
aims and objectives
the basics of regression: correlation
the basics of regression: what is it good for?
the basics of regression: describing the line
exploring linear regression
regression datagame
the pulling power of outliers		-
summary
TUTORIAL: Uncovering hidden influences		-
aims and objectives		-
a little knowledge is a dangerous thing		-
analysis of covariance: uncovering influence by linefitting		-
twoway analysis of variance: reducing bias and variance		-
matching and the removal of bias		-
summary		-
	v5.0 Demo	LITE Buy now

Statistics for the Terrified v5.0: what does it cover?

Statistics for the Terrified

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