t distribution | t Distribution |

t-test | ANOVA |

| One-Sample t-Test - Large Samples |

| One-Sample t-Test - Small Samples |

| One-Sample t-Test |

| Two-Sample t-Test |

| Two-Sample t-Test - Large Samples |

| Two-Sample t-Test - Small Sample Size |

| Comparing means |

| Paired Experiments |

| Exercise - Relative Humidity of US Cities |

| Exercise - Testing the Reaction Time of a Person |

| Exercise - Comparing two sample means |

| Welch-Test |

t-test for paired differences | Wilcoxon-Test for Paired Differences |

t-test for unequal variances | Welch-Test |

Tanimoto coefficient | Distance and Similarity Measures |

Taxonometrie multivariater Methoden | Übersicht zu multivariaten Methoden |

taxonomy of ANNs | Taxonomy of ANNs |

TDNN | Time Series - Neural Network Models |

temperature | Exercise - Determine time shift by autocorrelation |

test | Types of Error |

| Interpreting p values |

| Kolmogorov-Smirnov One-Sample Test |

| Test for Normality |

| Outlier Tests |

| Outlier Tests - Basic Rules |

| Outlier Test - Dean and Dixon |

| One-Sample t-Test - Large Samples |

| One-Sample t-Test - Small Samples |

| One Sample Chi-Square-Test |

| Two-Sample t-Test |

| Two-Sample t-Test - Large Samples |

| Two-Sample t-Test - Small Sample Size |

| Two-Sample F-Test |

| Chi-Square Test |

| Comparing means |

| Paired Experiments |

| Distribution-Free Tests |

| Hypothesis Testing |

| One-Sided vs. Two-Sided Tests |

| Power of a Test |

| Test: Correlation Coefficient |

| Randomization Tests |

| Rank Randomization Tests |

textbooks in statistics | Literature References - Textbooks |

theorem of Chebyshev | Chebyshev's Theorem |

thermal noise | Physical Origin of Noise |

third moment | Skewness |

tied observations | Spearman's Rank Correlation |

time and frequency | Time and Frequency |

time dependence of data | Time Dependence of Data |

time series | Literature References - Time Series |

| Signals as Time Series |

| Time Series - Neural Network Models |

| Time Series - Definition of ARIMA Models |

| Time Series - Establishing ARIMA models |

| Time Series - Forecasting |

| Time Series - Introduction |

| Time Series - Model Finding |

| Time Series - Trends |

| Time Dependence of Data |

time shift | Exercise - Determine time shift by autocorrelation |

time-averaging | Signal and Noise |

| Time-Averaging |

| Time-Averaging - Mathematical Details |

topological descriptors | Data Set - Boiling Points and Chemical Descriptors |

trace | Matrix Algebra - Fundamentals |

trains | Data Set - Delayed Trains |

transformation | Curvilinear Regression |

| Regression after Linearisation |

transformation of data space | Transformation of the Data Space |

| Transformation of the Data Space - Example: mass spectrometry |

transposed matrix | Transposed Matrix |

| PCA of Transposed Matrices |

treatment | Experimental Design |

trend analysis | Exercise - Weight Loss of Coins |

trends | Time Series - Trends |

Tukey-Fenster | Fensterfunktionen in der FFT |

two-sample F-test | Two-Sample F-Test |

two-sample t-test | Two-Sample t-Test - Large Samples |

two-sided tests | One-Sided vs. Two-Sided Tests |

types of error | Types of Error |

types of noise | Types of Noise |